Trust in Artificial Intelligent Agents Scale

First quantitative approbation. Data analysis workflow

Anton Angelgardt

HSE University

2021-05-23


Preprocess

Find preprocess workflow here.

Packages

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
library(lavaan)
## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.
## 
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
## 
##     cor2cov
library(semPlot)
library(knitr)
library(ggcorrplot)
theme_set(theme_bw())

Import data

taia <- read_csv("https://github.com/angelgardt/taia/raw/master/data/taia.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   id = col_character(),
##   oth_text = col_character(),
##   expoth_text = col_character(),
##   sex = col_character(),
##   edulvl1 = col_character(),
##   spec1 = col_character(),
##   edulvl2 = col_character(),
##   spec2 = col_character(),
##   jobfield = col_character(),
##   jobpos = col_character(),
##   city = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
str(taia)
## tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ id         : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
##  $ e_dighelp  : num [1:495] 5 NA 4 3.33 3 ...
##  $ n_dighelp  : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
##  $ e_socnet   : num [1:495] 0 NA 3.2 3.6 4.5 ...
##  $ f_socnet   : num [1:495] 3 NA 2.4 1.6 2 ...
##  $ n_socnet   : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
##  $ gt_score   : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
##  $ pr01       : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
##  $ pr02       : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
##  $ pr03       : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
##  $ pr04       : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
##  $ pr05       : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
##  $ pr06       : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
##  $ pr07       : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
##  $ pr08       : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
##  $ pr09       : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
##  $ pr10       : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
##  $ co01       : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
##  $ co02       : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
##  $ co03       : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
##  $ co04       : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
##  $ co05       : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
##  $ co06       : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
##  $ co07       : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
##  $ co08       : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
##  $ co09       : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
##  $ co10       : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
##  $ ut01       : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
##  $ ut02       : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
##  $ ut03       : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
##  $ ut04       : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
##  $ ut05       : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
##  $ ut06       : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
##  $ ut07       : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
##  $ ut08       : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
##  $ ut09       : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
##  $ ut10       : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
##  $ ut11       : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
##  $ ut12       : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
##  $ fa01       : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
##  $ fa02       : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
##  $ fa03       : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
##  $ fa04       : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
##  $ fa05       : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
##  $ fa06       : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
##  $ fa07       : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
##  $ fa08       : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
##  $ fa09       : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
##  $ fa10       : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
##  $ de01       : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
##  $ de02       : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
##  $ de03       : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
##  $ de04       : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
##  $ de05       : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
##  $ de06       : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
##  $ de07       : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
##  $ de08       : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
##  $ de09       : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
##  $ de10       : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
##  $ de11       : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
##  $ un01       : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
##  $ un02       : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
##  $ un03       : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
##  $ un04       : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
##  $ un05       : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
##  $ un06       : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
##  $ un07       : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
##  $ un08       : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
##  $ un09       : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
##  $ un10       : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
##  $ un11       : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
##  $ un12       : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
##  $ gt01       : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
##  $ gt02       : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
##  $ gt03       : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
##  $ gt04       : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
##  $ gt05       : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
##  $ gt06       : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
##  $ socnet     : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
##  $ vk         : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
##  $ fb         : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
##  $ tw         : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
##  $ in         : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
##  $ tt         : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
##  $ yt         : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
##  $ freqvk     : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
##  $ freqfb     : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
##  $ freqtw     : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
##  $ freqin     : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
##  $ freqtt     : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
##  $ freqyt     : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
##  $ expvk      : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
##  $ expfb      : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
##  $ exptw      : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
##  $ expin      : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
##  $ exptt      : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
##  $ expyt      : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
##  $ dighelp    : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
##  $ siri       : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
##   [list output truncated]
##  - attr(*, "spec")=
##   .. cols(
##   ..   id = col_character(),
##   ..   e_dighelp = col_double(),
##   ..   n_dighelp = col_double(),
##   ..   e_socnet = col_double(),
##   ..   f_socnet = col_double(),
##   ..   n_socnet = col_double(),
##   ..   gt_score = col_double(),
##   ..   pr01 = col_double(),
##   ..   pr02 = col_double(),
##   ..   pr03 = col_double(),
##   ..   pr04 = col_double(),
##   ..   pr05 = col_double(),
##   ..   pr06 = col_double(),
##   ..   pr07 = col_double(),
##   ..   pr08 = col_double(),
##   ..   pr09 = col_double(),
##   ..   pr10 = col_double(),
##   ..   co01 = col_double(),
##   ..   co02 = col_double(),
##   ..   co03 = col_double(),
##   ..   co04 = col_double(),
##   ..   co05 = col_double(),
##   ..   co06 = col_double(),
##   ..   co07 = col_double(),
##   ..   co08 = col_double(),
##   ..   co09 = col_double(),
##   ..   co10 = col_double(),
##   ..   ut01 = col_double(),
##   ..   ut02 = col_double(),
##   ..   ut03 = col_double(),
##   ..   ut04 = col_double(),
##   ..   ut05 = col_double(),
##   ..   ut06 = col_double(),
##   ..   ut07 = col_double(),
##   ..   ut08 = col_double(),
##   ..   ut09 = col_double(),
##   ..   ut10 = col_double(),
##   ..   ut11 = col_double(),
##   ..   ut12 = col_double(),
##   ..   fa01 = col_double(),
##   ..   fa02 = col_double(),
##   ..   fa03 = col_double(),
##   ..   fa04 = col_double(),
##   ..   fa05 = col_double(),
##   ..   fa06 = col_double(),
##   ..   fa07 = col_double(),
##   ..   fa08 = col_double(),
##   ..   fa09 = col_double(),
##   ..   fa10 = col_double(),
##   ..   de01 = col_double(),
##   ..   de02 = col_double(),
##   ..   de03 = col_double(),
##   ..   de04 = col_double(),
##   ..   de05 = col_double(),
##   ..   de06 = col_double(),
##   ..   de07 = col_double(),
##   ..   de08 = col_double(),
##   ..   de09 = col_double(),
##   ..   de10 = col_double(),
##   ..   de11 = col_double(),
##   ..   un01 = col_double(),
##   ..   un02 = col_double(),
##   ..   un03 = col_double(),
##   ..   un04 = col_double(),
##   ..   un05 = col_double(),
##   ..   un06 = col_double(),
##   ..   un07 = col_double(),
##   ..   un08 = col_double(),
##   ..   un09 = col_double(),
##   ..   un10 = col_double(),
##   ..   un11 = col_double(),
##   ..   un12 = col_double(),
##   ..   gt01 = col_double(),
##   ..   gt02 = col_double(),
##   ..   gt03 = col_double(),
##   ..   gt04 = col_double(),
##   ..   gt05 = col_double(),
##   ..   gt06 = col_double(),
##   ..   socnet = col_double(),
##   ..   vk = col_double(),
##   ..   fb = col_double(),
##   ..   tw = col_double(),
##   ..   `in` = col_double(),
##   ..   tt = col_double(),
##   ..   yt = col_double(),
##   ..   freqvk = col_double(),
##   ..   freqfb = col_double(),
##   ..   freqtw = col_double(),
##   ..   freqin = col_double(),
##   ..   freqtt = col_double(),
##   ..   freqyt = col_double(),
##   ..   expvk = col_double(),
##   ..   expfb = col_double(),
##   ..   exptw = col_double(),
##   ..   expin = col_double(),
##   ..   exptt = col_double(),
##   ..   expyt = col_double(),
##   ..   dighelp = col_double(),
##   ..   siri = col_double(),
##   ..   alice = col_double(),
##   ..   salut = col_double(),
##   ..   oleg = col_double(),
##   ..   alex = col_double(),
##   ..   mia = col_double(),
##   ..   mts = col_double(),
##   ..   ggle = col_double(),
##   ..   oth = col_double(),
##   ..   oth_text = col_character(),
##   ..   expsiri = col_double(),
##   ..   expalice = col_double(),
##   ..   expsalut = col_double(),
##   ..   expoleg = col_double(),
##   ..   expalex = col_double(),
##   ..   expmia = col_double(),
##   ..   expmts = col_double(),
##   ..   expggle = col_double(),
##   ..   expoth = col_double(),
##   ..   expoth_text = col_character(),
##   ..   selfdrcar = col_double(),
##   ..   selfdrexp = col_double(),
##   ..   selfdrsafe = col_double(),
##   ..   eduai = col_double(),
##   ..   eduaiexp = col_double(),
##   ..   age = col_double(),
##   ..   sex = col_character(),
##   ..   edulvl1 = col_character(),
##   ..   spec1 = col_character(),
##   ..   edu2 = col_double(),
##   ..   edulvl2 = col_character(),
##   ..   spec2 = col_character(),
##   ..   jobfield = col_character(),
##   ..   jobpos = col_character(),
##   ..   city = col_character()
##   .. )

Preparation

Vectors of TAIA items:

pr_items_0 <- colnames(taia)[8:17]
co_items_0 <- colnames(taia)[18:27]
ut_items_0 <- colnames(taia)[28:39]
fa_items_0 <- colnames(taia)[40:49]
de_items_0 <- colnames(taia)[50:60]
un_items_0 <- colnames(taia)[61:72]
taia_items_0 <- colnames(taia)[8:72]

Vector of GT items:

gt_items <- colnames(taia)[73:78]

Column names for further formatting:

col_names <- c("", "Num. of obs.", "Mean", "SD",
               "Median", "Trimmed Mean", "MAD",
               "Min", "Max", "Range",
               "Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
                    "Average interitem correlation", "S/N",
                    "Alpha SE", "Scale Mean", "Total Score SD",
                    "Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
                         "Std Cor",
                         "Cor Overlap Corrected",
                         "Cor if drop",
                         "Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
                "Guttman's Lambda 6",   "Average interitem correlation",
                "S/N",  "Alpha SE", "Var(r)","Median interitem correlation")

Exploratory analysis

TAIA descriptive statistics

Predictability

taia %>% 
  select(all_of(pr_items_0)) %>% 
  describe() %>% 
  kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)
Predictability
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
pr01 1 495 2.84 0.99 3 2.87 1.48 0 5 5 -0.28 0.37 0.04
pr02 2 495 2.73 0.97 3 2.77 1.48 0 5 5 -0.19 0.10 0.04
pr03 3 495 2.89 1.01 3 2.91 1.48 0 5 5 -0.15 -0.05 0.05
pr04 4 495 2.84 1.04 3 2.87 1.48 0 5 5 -0.18 -0.04 0.05
pr05 5 495 2.22 1.20 2 2.22 1.48 0 5 5 0.03 -0.31 0.05
pr06 6 495 3.04 1.07 3 3.06 1.48 0 5 5 -0.26 0.07 0.05
pr07 7 495 2.59 1.11 3 2.61 1.48 0 5 5 -0.15 -0.10 0.05
pr08 8 495 3.05 0.91 3 3.09 0.00 0 5 5 -0.56 1.26 0.04
pr09 9 495 2.89 0.95 3 2.94 0.00 0 5 5 -0.50 1.05 0.04
pr10 10 495 2.83 1.04 3 2.90 1.48 0 5 5 -0.41 0.28 0.05
taia %>% select(all_of(pr_items_0)) %>% 
  pivot_longer(cols = all_of(pr_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkred") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Predictability") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • pr01 OK
    • Я считаю, что интеллектуальные системы надежны
  • pr02 OK
    • Я считаю, что результаты работы интеллектуальных систем хорошо предсказуемы
  • pr03 OK
    • Я считаю, что интеллектуальные системы ненадежны (R)
  • pr04 OK
    • Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
  • pr05 positive skewness
    • Я считаю, что поездки в машине, управляемой искусственным интеллектом, безопаснее, чем в обычной
  • pr06 OK
    • Я считаю, что медицинская диагностика с применением интеллектуальных систем надеждее, чем без них
  • pr07 OK
    • Я считаю, что финансовые операции под контролем искусственного интеллекта безопаснее, чем обычные
  • pr08 high kurtosis
    • Я считаю, что интеллектуальные системы, обеспечивающие безопасность домов, хорошо справляются со своей задачей
  • pr09 high kurtosis
    • Я считаю, что рекомендательные системы чаще всего правильно определяют предпочтения пользователей
  • pr10 OK

Consistency

taia %>% 
  select(all_of(co_items_0)) %>% 
  describe() %>% 
  kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)
Consistency
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
co01 1 495 2.49 1.08 3 2.51 1.48 0 5 5 -0.17 0.13 0.05
co02 2 495 2.51 1.04 3 2.53 1.48 0 5 5 -0.19 -0.06 0.05
co03 3 495 2.86 1.02 3 2.92 1.48 0 5 5 -0.40 0.31 0.05
co04 4 495 3.47 1.09 4 3.54 1.48 0 5 5 -0.57 0.32 0.05
co05 5 495 2.20 1.11 2 2.17 1.48 0 5 5 0.10 -0.17 0.05
co06 6 495 2.52 1.11 3 2.53 1.48 0 5 5 -0.13 -0.15 0.05
co07 7 495 1.59 1.13 2 1.52 1.48 0 5 5 0.54 0.12 0.05
co08 8 495 1.90 1.04 2 1.86 1.48 0 5 5 0.40 0.28 0.05
co09 9 495 2.05 1.07 2 2.01 1.48 0 5 5 0.35 0.12 0.05
co10 10 495 2.44 1.10 2 2.44 1.48 0 5 5 -0.04 -0.06 0.05
taia %>% select(all_of(co_items_0)) %>% 
  pivot_longer(cols = all_of(co_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "chocolate3") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Consistency") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • co01 OK
    • Интеллектуальные системы надежны, так как те системы, с которыми я сталкивался (сталкивалась), были надежными
  • co02 OK
    • Я считаю, что если система при тестировании работает корректно, то и дальше она будет работать корректно
  • co03 high kurtosis
    • Я считаю, что интеллектуальные системы совершают меньше технических ошибок, чем люди
  • co04 high negative skewness
    • Со временем любая интеллектуальная система будет совершать всё меньше ошибок
  • co05 positive skewness
    • Если интеллектуальные системы, с которыми я сталкивался (сталкивалась), были надежными, то я могу считать надёжными другие системы
  • co06 positive skewness
    • Я могу судить о надёжности новой интеллектуальной системы по опыту работы с другими системами
  • co07 high positive skewness
    • Я могу сказать, что система работает корректно, только протестировав её (R)
  • co08 positive skewness
    • Если одна интеллектуальная система подводит меня, то и с другими будет то же самое
  • co09positive skewness
    • Если одна интеллектуальная система соответствует моим ожиданиям, то с другими будет то же самое
  • co10 positive skewness
    • Я могу сформировать ожидания относительно работы интеллектуальных систем в целом на основе опыта взаимодействия с одной системой

Utility

taia %>% 
  select(all_of(ut_items_0)) %>% 
  describe() %>% 
  kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)
Utility
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
ut01 1 495 3.78 1.05 4 3.88 1.48 0 5 5 -0.86 1.12 0.05
ut02 2 495 3.52 1.05 3 3.59 1.48 0 5 5 -0.53 0.50 0.05
ut03 3 495 3.56 1.11 4 3.64 1.48 0 5 5 -0.57 0.15 0.05
ut04 4 495 3.09 1.11 3 3.15 1.48 0 5 5 -0.43 0.03 0.05
ut05 5 495 3.05 1.21 3 3.09 1.48 0 5 5 -0.33 -0.15 0.05
ut06 6 495 3.27 1.10 3 3.31 1.48 0 5 5 -0.61 0.68 0.05
ut07 7 495 3.20 1.13 3 3.23 1.48 0 5 5 -0.28 -0.22 0.05
ut08 8 495 3.44 1.05 3 3.49 1.48 0 5 5 -0.55 0.44 0.05
ut09 9 495 3.18 1.17 3 3.23 1.48 0 5 5 -0.47 0.16 0.05
ut10 10 495 2.17 1.11 2 2.16 1.48 0 5 5 0.08 -0.23 0.05
ut11 11 495 2.67 1.23 3 2.69 1.48 0 5 5 -0.11 -0.41 0.06
ut12 12 495 3.16 1.15 3 3.21 1.48 0 5 5 -0.42 0.03 0.05
taia %>% select(all_of(ut_items_0)) %>% 
  pivot_longer(cols = all_of(ut_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "goldenrod3") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Utility") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • ut01 extremely high negative skewness
    • Я считаю, что интеллектуальные технологии — неотъемлемая часть развития общества
  • ut02 high negative skewness
    • Я считаю, что человечество нуждается в интеллектуальных системах
  • ut03 high negative skewness
    • Мне кажется, человечеству будет лучше без интеллектуальных систем (R)
  • ut04 negative skewness
    • Я считаю, что развитие интеллектуальных систем — это оправданный риск для общества
  • ut05 OK
    • По моему мнению, без интеллектуальных систем общественный прогресс остановился бы
  • ut06 negative skewness
    • Я считаю, что интеллектуальные системы существенно повышают качество медицинской диагностики
  • ut07 light negative skewness
    • Мне кажется, что оформить услуги с цифровыми помощниками гораздо проще, чем без них
  • ut08 negative skewness
    • Мне кажется, рекомендательные сервисы существенно сокращают время поиска нужной информации
  • ut09 negative skewness
    • Я считаю, что если полиция будет использовать интеллектуальные системы, то количество раскрытых преступлений увеличится
  • ut10 positive skewness
    • Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
  • ut11 OK
    • Я считаю, что если на дорогах появятся машины, управляемые искусственным интеллектом, то число аварий снизится
  • ut12 negative skewness
    • Я считаю, что использование интеллектуальных систем в обучении повышает качество образования

Faith

taia %>% 
  select(all_of(fa_items_0)) %>% 
  describe() %>% 
  kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)
Faith
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
fa01 1 495 2.42 1.10 2 2.42 1.48 0 5 5 -0.02 -0.29 0.05
fa02 2 495 2.16 1.18 2 2.15 1.48 0 5 5 0.18 -0.42 0.05
fa03 3 495 1.51 1.13 1 1.42 1.48 0 5 5 0.66 0.16 0.05
fa04 4 495 1.57 1.08 1 1.51 1.48 0 5 5 0.55 0.17 0.05
fa05 5 495 2.46 1.10 2 2.48 1.48 0 5 5 -0.15 -0.10 0.05
fa06 6 495 2.47 1.08 3 2.49 1.48 0 5 5 -0.18 0.06 0.05
fa07 7 495 2.37 1.09 2 2.38 1.48 0 5 5 -0.14 -0.17 0.05
fa08 8 495 2.21 1.14 2 2.17 1.48 0 5 5 0.28 -0.13 0.05
fa09 9 495 2.29 1.18 2 2.27 1.48 0 5 5 0.15 -0.40 0.05
fa10 10 495 2.64 1.20 3 2.62 1.48 0 5 5 0.06 -0.28 0.05
taia %>% select(all_of(fa_items_0)) %>% 
  pivot_longer(cols = all_of(fa_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkgreen") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Faith") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • fa01 OK
    • Я готов (готова) довериться интеллектуальным системам, даже если я не до конца понимаю, как они работают
  • fa02 light positive skewness
    • Чтобы доверять интеллектуальной системе, мне нужно точно понимать, как работают её алгоритмы (R)
  • fa03 hard positive skewness
    • Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
  • fa04 hard positive skewness
    • Если интеллектуальная система управлениями финансами не реагирует на запросы, я все равно буду уверен, что она работает корректно
  • fa05 OK
    • Я готов (готова) доверять результатам работы интеллектуальных систем, даже если я не знаю, как они работают
  • fa06 OK
    • Я готов (готова) довериться работе интеллектуальных систем так же, как работе профессионалов
  • fa07 OK
    • Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
  • fa08 light positive skewness
    • Мне нужно знать детали работы алгоритма, чтобы быть уверенным (уверенной) в качестве результата его работы (R)
  • fa09 OK
    • Если я не понимаю, как работает интеллектуальная система, я не могу быть уверенным (уверенной) в результате её работы (R)
  • fa10 OK
    • Мне не важно, как работает интеллектуальная система, если она должным образом справляется со своей задачей

Dependability

taia %>% 
  select(all_of(de_items_0)) %>% 
  describe() %>% 
  kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)
Dependability
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
de01 1 495 2.59 1.10 3 2.64 1.48 0 5 5 -0.42 0.08 0.05
de02 2 495 2.17 1.15 2 2.18 1.48 0 5 5 0.00 -0.34 0.05
de03 3 495 2.17 1.19 2 2.17 1.48 0 5 5 0.04 -0.30 0.05
de04 4 495 1.90 1.05 2 1.85 1.48 0 5 5 0.55 0.66 0.05
de05 5 495 3.57 1.16 4 3.68 1.48 0 5 5 -0.78 0.48 0.05
de06 6 495 2.23 1.23 2 2.25 1.48 0 5 5 0.01 -0.43 0.06
de07 7 495 2.82 1.00 3 2.86 1.48 0 5 5 -0.30 0.31 0.04
de08 8 495 2.65 1.06 3 2.70 1.48 0 5 5 -0.40 0.14 0.05
de09 9 495 3.44 1.20 4 3.54 1.48 0 5 5 -0.61 -0.14 0.05
de10 10 495 2.25 1.18 2 2.28 1.48 0 5 5 -0.22 -0.42 0.05
de11 11 495 2.31 1.20 2 2.31 1.48 0 5 5 0.00 -0.52 0.05
taia %>% select(all_of(de_items_0)) %>% 
  pivot_longer(cols = all_of(de_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkblue") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Dependability") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • de01 negative skewness
    • Я готов (готова) следовать рекомендациям рекомендательных систем социальных сетей
  • de02 OK
    • Я готов (готова) делегировать управление финансами интеллектуальному помощнику
  • de03 OK
    • Если интеллектуальная система запрашивает мои личные данные, я могу быть уверен (уверенна) в их сохранности
  • de04 hard positive skewness
    • Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё
  • de05 hard negative skewness
    • Я бы мог (могла) жить в «умном доме»
  • de06 negative kurtosis
    • Если бы я ехал (ехала) в автомобиле, управляемом искусственным интеллектом, я бы был спокоен за свою безопасность
  • de07 OK
    • Если меня обследуют с помощью интеллектуальных медицинских технологий, я могу доверять выставленному диагнозу
  • de08 OK
    • Я считаю, что при покупках с интеллектуальными помощниками риск ошибиться меньше, чем без них
  • de09 negative skewness
    • Мне было бы некомфортно жить в «умном доме» (R)
  • de10 negative kurtosis
    • Я могу доверить искусственному интеллекту задачи, от которых зависит моя личная безопасность
  • de11 negative kurtosis
    • Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)

Understanding

taia %>%
  select(all_of(un_items_0)) %>% 
  describe() %>% 
  kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)
Understanding
Num. of obs. Mean SD Median Trimmed Mean MAD Min Max Range Skewness Kurtuosis SE
un01 1 495 2.93 1.05 3 3.01 1.48 0 5 5 -0.48 0.31 0.05
un02 2 495 2.47 1.14 3 2.49 1.48 0 5 5 -0.17 -0.27 0.05
un03 3 495 3.02 1.17 3 3.10 1.48 0 5 5 -0.55 0.01 0.05
un04 4 495 2.61 1.09 3 2.65 1.48 0 5 5 -0.33 -0.22 0.05
un05 5 495 2.82 1.10 3 2.90 1.48 0 5 5 -0.51 0.24 0.05
un06 6 495 2.29 1.23 2 2.26 1.48 0 5 5 0.19 -0.62 0.06
un07 7 495 2.13 1.18 2 2.14 1.48 0 5 5 -0.02 -0.54 0.05
un08 8 495 2.90 1.16 3 2.96 1.48 0 5 5 -0.45 0.00 0.05
un09 9 495 2.32 1.23 2 2.37 1.48 0 5 5 -0.18 -0.75 0.06
un10 10 495 2.24 1.15 2 2.23 1.48 0 5 5 0.05 -0.44 0.05
un11 11 495 2.63 1.20 3 2.66 1.48 0 5 5 -0.25 -0.37 0.05
un12 12 495 2.89 1.12 3 2.96 1.48 0 5 5 -0.46 0.13 0.05
taia %>% select(all_of(un_items_0)) %>% 
  pivot_longer(cols = all_of(un_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "purple4") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Understanding") +
  theme(plot.title = element_text(hjust = .5))
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

  • un01 OK
    • Я понимаю, как происходит взаимодействие человека с интеллектуальными системами
  • un02 OK
    • Я понимаю, как работают алгоритмы интеллектуальных систем
  • un03 negative skewness
    • Я стараюсь разобраться в том, как работают интеллектуальные системы
  • un04 negative skewness
    • Я понимаю, как работают отдельные элементы интеллектуальных систем
  • un05 positive kurtosis
    • Я понимаю общий принцип работы интеллектуальных систем
  • un06 negative kurtosis
    • Я плохо разбираюсь в тонкостях работы интеллектуальных систем (R)
  • un07 negative kurtosis
    • Мне понятно, как работают интеллектуальные алгоритмы, применяемые в медицинской диагностике
  • un08 OK
    • Мне понятно, как работают алгоритмы рекомендательных систем социальных сетей
  • un09 extra negative kurtosis
    • Я понимаю, как работают алгоритмы автомобилей, управляемых искусственным интеллектом
  • un10 negative kurtosis
    • Я понимаю, как работают алгоритмы интеллектуальных систем, которые управляют финансами
  • un11 OK
    • Мне понятно, как устроены алгоритмы машинного перевода текстов
  • un12 OK
    • Мне понятно, как работают алгоритмы систем типа «умный дом»

Correlations

Predictability

ggcorrplot(cor(taia %>% select(all_of(pr_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Predictability. Interitems correlations",
           show.legend = FALSE)

Consistency

ggcorrplot(cor(taia %>% select(all_of(co_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Consistency. Interitems correlations",
           show.legend = FALSE)

Utility

ggcorrplot(cor(taia %>% select(all_of(ut_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Utility. Interitems correlations",
           show.legend = FALSE)

Faith

ggcorrplot(cor(taia %>% select(all_of(fa_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Faith. Interitems correlations",
           show.legend = FALSE)

Dependability

ggcorrplot(cor(taia %>% select(all_of(de_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Dependability. Interitems correlations",
           show.legend = FALSE)

Understanding

ggcorrplot(cor(taia %>% select(all_of(un_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Understanding. Interitems correlations",
           show.legend = FALSE)

All TAIA items correlations

ggcorrplot(cor(taia %>% select(all_of(taia_items_0))),
           type = "lower",
           colors = c("indianred1", "white", "royalblue1"),
           title = "TAIA. Interitems correlations", tl.cex = 5, tl.srt = 90,
           legend.title = "Value")

qgraph::qgraph(
  cor(taia %>% select(all_of(taia_items_0))),
  layout = "spring",
  posCol = "royalblue",
  negCol = "indianred"
)

Psychometric Analysis

Subscales

Predictability

pr1 <- psych::alpha(
  taia %>% select(all_of(pr_items_0)),
  cumulative = TRUE,
  title = "Predictability Factor",
  check.keys = FALSE
)
kable(pr1$total,
      caption = "Perdictability. Subscale statistics", 
      label = 7, digits = 2,
      col.names = total_colnames
      )
Perdictability. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.81 0.81 0.82 0.3 4.25 0.01 27.92 6.23 0.33
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
      caption = "Predictability. Items statistics",
      label = 8, digits = 2,
      col.names = item_stats_colnames)
Predictability. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
pr01 495 0.79 0.80 0.80 0.72 0.57 0.99
pr02 495 0.66 0.66 0.62 0.56 0.55 0.97
pr03 495 0.45 0.45 0.37 0.31 0.58 1.01
pr04 495 0.32 0.32 0.21 0.16 0.57 1.04
pr05 495 0.61 0.59 0.51 0.46 0.44 1.20
pr06 495 0.62 0.62 0.56 0.50 0.61 1.07
pr07 495 0.74 0.73 0.70 0.63 0.52 1.11
pr08 495 0.72 0.73 0.71 0.64 0.61 0.91
pr09 495 0.61 0.62 0.56 0.50 0.58 0.95
pr10 495 0.55 0.56 0.47 0.42 0.57 1.04
pr1$item.stats %>%
  ggplot(aes(x = row.names(pr1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Predictability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(pr1$alpha.drop,
      caption = "Predictability. Subscale statistics when item drop",
      label = 9, digits = 2, col.names = alpha_drop_colnames)
Predictability. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
pr01 0.76 0.77 0.78 0.27 3.27 0.02 0.02 0.29
pr02 0.78 0.79 0.80 0.29 3.66 0.01 0.03 0.32
pr03 0.81 0.81 0.81 0.32 4.33 0.01 0.03 0.36
pr04 0.82 0.83 0.82 0.35 4.78 0.01 0.02 0.36
pr05 0.79 0.80 0.81 0.30 3.88 0.01 0.03 0.33
pr06 0.79 0.79 0.81 0.30 3.79 0.01 0.03 0.33
pr07 0.77 0.78 0.79 0.28 3.46 0.02 0.02 0.30
pr08 0.77 0.78 0.79 0.28 3.46 0.02 0.02 0.30
pr09 0.79 0.79 0.80 0.30 3.80 0.01 0.03 0.35
pr10 0.80 0.80 0.81 0.31 3.99 0.01 0.03 0.35
kable(pr1$response.freq,
      caption = "Predictability. Non missing response frequency for each item",
      label = 10, digits = 2)
Predictability. Non missing response frequency for each item
0 1 2 3 4 5 miss
pr01 0.02 0.06 0.24 0.45 0.19 0.04 0
pr02 0.01 0.08 0.28 0.43 0.17 0.03 0
pr03 0.01 0.06 0.26 0.40 0.22 0.05 0
pr04 0.02 0.07 0.27 0.38 0.21 0.05 0
pr05 0.09 0.17 0.33 0.29 0.09 0.03 0
pr06 0.02 0.06 0.20 0.41 0.23 0.08 0
pr07 0.04 0.12 0.28 0.38 0.14 0.04 0
pr08 0.02 0.03 0.16 0.52 0.24 0.04 0
pr09 0.02 0.05 0.18 0.53 0.17 0.04 0
pr10 0.02 0.08 0.21 0.45 0.20 0.04 0

Consistency

co1 <- psych::alpha(
  taia %>% select(all_of(co_items_0)),
  cumulative = TRUE,
  title = "Consistency Factor",
  check.keys = FALSE
)
## Warning in psych::alpha(taia %>% select(all_of(co_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( co07 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
kable(co1$total,
      caption = "Consistency. Subscale statistics", 
      label = 11, digits = 2,
      col.names = total_colnames)
Consistency. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.76 0.77 0.8 0.25 3.27 0.01 24.03 6.1 0.27
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
      caption = "Consistency. Items statistics",
      label = 12, digits = 2,
      col.names = item_stats_colnames)
Consistency. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
co01 495 0.74 0.74 0.72 0.64 0.50 1.08
co02 495 0.68 0.69 0.65 0.57 0.50 1.04
co03 495 0.55 0.55 0.48 0.41 0.57 1.02
co04 495 0.40 0.41 0.30 0.24 0.69 1.09
co05 495 0.79 0.78 0.79 0.70 0.44 1.11
co06 495 0.65 0.65 0.61 0.53 0.50 1.11
co07 495 -0.03 -0.04 -0.22 -0.22 0.32 1.13
co08 495 0.45 0.46 0.36 0.30 0.38 1.04
co09 495 0.76 0.77 0.76 0.67 0.41 1.07
co10 495 0.67 0.67 0.62 0.56 0.49 1.10
co1$item.stats %>%
  ggplot(aes(x = row.names(co1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Consistency. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(co1$alpha.drop,
      caption = "Consistency. Subscale statistics when item drop",
      label = 13, digits = 2,
      col.names = alpha_drop_colnames)
Consistency. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
co01 0.71 0.72 0.76 0.22 2.52 0.02 0.06 0.26
co02 0.72 0.73 0.77 0.23 2.66 0.02 0.06 0.26
co03 0.74 0.75 0.79 0.25 2.98 0.02 0.07 0.27
co04 0.77 0.77 0.80 0.27 3.36 0.01 0.06 0.35
co05 0.70 0.71 0.75 0.21 2.42 0.02 0.06 0.26
co06 0.73 0.73 0.77 0.23 2.74 0.02 0.06 0.27
co07 0.83 0.82 0.83 0.34 4.70 0.01 0.03 0.36
co08 0.76 0.76 0.79 0.26 3.23 0.02 0.07 0.34
co09 0.71 0.71 0.75 0.22 2.47 0.02 0.06 0.26
co10 0.72 0.73 0.77 0.23 2.69 0.02 0.07 0.26
kable(co1$response.freq,
      caption = "Consistency. Non missing response frequency for each item",
      label = 14, digits = 2)
Consistency. Non missing response frequency for each item
0 1 2 3 4 5 miss
co01 0.05 0.11 0.31 0.39 0.11 0.03 0
co02 0.03 0.12 0.32 0.38 0.13 0.02 0
co03 0.02 0.07 0.22 0.44 0.21 0.04 0
co04 0.01 0.04 0.10 0.35 0.32 0.18 0
co05 0.06 0.19 0.37 0.27 0.09 0.02 0
co06 0.04 0.14 0.28 0.38 0.13 0.03 0
co07 0.18 0.31 0.32 0.14 0.04 0.02 0
co08 0.08 0.27 0.42 0.16 0.06 0.01 0
co09 0.06 0.24 0.40 0.22 0.06 0.02 0
co10 0.04 0.14 0.33 0.34 0.11 0.03 0

Utility

ut1 <- psych::alpha(
  taia %>% select(all_of(ut_items_0)),
  cumulative = TRUE,
  title = "Utility Factor",
  check.keys = FALSE
)
kable(ut1$total,
      caption = "Utility. Subscale statistics", 
      label = 15, digits = 2,
      col.names = total_colnames)
Utility. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.86 0.86 0.87 0.34 6.17 0.01 38.09 8.44 0.37
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
      caption = "Utility. Items statistics",
      label = 16, digits = 2,
      col.names = item_stats_colnames)
Utility. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
ut01 495 0.77 0.78 0.78 0.71 0.76 1.05
ut02 495 0.82 0.83 0.84 0.77 0.70 1.05
ut03 495 0.57 0.57 0.52 0.47 0.71 1.11
ut04 495 0.51 0.51 0.44 0.40 0.62 1.11
ut05 495 0.69 0.69 0.65 0.61 0.61 1.21
ut06 495 0.76 0.76 0.74 0.70 0.65 1.10
ut07 495 0.63 0.63 0.59 0.54 0.64 1.13
ut08 495 0.65 0.66 0.62 0.57 0.69 1.05
ut09 495 0.68 0.68 0.64 0.59 0.64 1.17
ut10 495 0.17 0.17 0.06 0.04 0.43 1.11
ut11 495 0.56 0.55 0.48 0.45 0.53 1.23
ut12 495 0.71 0.71 0.68 0.64 0.63 1.15
ut1$item.stats %>%
  ggplot(aes(x = row.names(ut1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Utility. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(ut1$alpha.drop,
      caption = "Utility. Subscale statistics when item drop",
      label = 17, digits = 2,
      col.names = alpha_drop_colnames)
Utility. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
ut01 0.84 0.84 0.85 0.32 5.16 0.01 0.03 0.34
ut02 0.83 0.83 0.84 0.31 5.00 0.01 0.03 0.33
ut03 0.85 0.85 0.86 0.35 5.84 0.01 0.04 0.41
ut04 0.86 0.86 0.87 0.36 6.06 0.01 0.03 0.41
ut05 0.84 0.84 0.86 0.33 5.45 0.01 0.04 0.37
ut06 0.84 0.84 0.85 0.32 5.21 0.01 0.03 0.33
ut07 0.85 0.85 0.86 0.34 5.65 0.01 0.04 0.37
ut08 0.85 0.85 0.86 0.34 5.55 0.01 0.03 0.37
ut09 0.84 0.85 0.86 0.33 5.49 0.01 0.03 0.37
ut10 0.88 0.88 0.88 0.40 7.40 0.01 0.01 0.41
ut11 0.85 0.86 0.87 0.35 5.92 0.01 0.04 0.41
ut12 0.84 0.84 0.86 0.33 5.37 0.01 0.03 0.35
kable(ut1$response.freq,
      caption = "Utility. Non missing response frequency for each item",
      label = 18, digits = 2)
Utility. Non missing response frequency for each item
0 1 2 3 4 5 miss
ut01 0.01 0.01 0.06 0.29 0.34 0.28 0
ut02 0.01 0.02 0.09 0.38 0.30 0.20 0
ut03 0.01 0.04 0.09 0.33 0.31 0.22 0
ut04 0.02 0.08 0.15 0.40 0.27 0.09 0
ut05 0.03 0.06 0.20 0.35 0.23 0.12 0
ut06 0.03 0.03 0.13 0.39 0.30 0.12 0
ut07 0.01 0.05 0.19 0.35 0.26 0.14 0
ut08 0.01 0.03 0.11 0.36 0.34 0.15 0
ut09 0.03 0.05 0.15 0.38 0.25 0.13 0
ut10 0.07 0.19 0.37 0.26 0.09 0.02 0
ut11 0.05 0.12 0.27 0.32 0.18 0.07 0
ut12 0.02 0.07 0.15 0.38 0.26 0.12 0

Faith

fa1 <- psych::alpha(
  taia %>% select(all_of(fa_items_0)),
  cumulative = TRUE,
  title = "Faith Factor",
  check.keys = FALSE
)
kable(fa1$total,
      caption = "Faith. Subscale statistics", 
      label = 19, digits = 2,
      col.names = total_colnames)
Faith. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.77 0.77 0.81 0.25 3.26 0.02 22.1 6.41 0.24
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
      caption = "Faith. Items statistics",
      label = 20, digits = 2,
      col.names = item_stats_colnames)
Faith. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
fa01 495 0.76 0.76 0.76 0.67 0.48 1.10
fa02 495 0.61 0.60 0.56 0.47 0.43 1.18
fa03 495 0.27 0.28 0.16 0.10 0.30 1.13
fa04 495 0.41 0.42 0.33 0.26 0.31 1.08
fa05 495 0.77 0.78 0.78 0.69 0.49 1.10
fa06 495 0.55 0.56 0.50 0.42 0.49 1.08
fa07 495 0.42 0.42 0.31 0.26 0.47 1.09
fa08 495 0.58 0.57 0.52 0.44 0.44 1.14
fa09 495 0.66 0.65 0.62 0.53 0.46 1.18
fa10 495 0.63 0.62 0.57 0.50 0.53 1.20
fa1$item.stats %>%
  ggplot(aes(x = row.names(fa1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Faith. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(fa1$alpha.drop,
      caption = "Faith. Subscale statistics when item drop",
      label = 21, digits = 2,
      col.names = alpha_drop_colnames)
Faith. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
fa01 0.71 0.71 0.76 0.22 2.47 0.02 0.04 0.22
fa02 0.74 0.74 0.78 0.24 2.87 0.02 0.04 0.24
fa03 0.79 0.79 0.82 0.29 3.70 0.01 0.04 0.30
fa04 0.77 0.77 0.81 0.27 3.30 0.02 0.04 0.26
fa05 0.71 0.71 0.76 0.21 2.43 0.02 0.04 0.22
fa06 0.75 0.75 0.79 0.25 2.95 0.02 0.05 0.29
fa07 0.77 0.77 0.81 0.27 3.30 0.02 0.05 0.30
fa08 0.74 0.75 0.79 0.25 2.92 0.02 0.04 0.24
fa09 0.73 0.73 0.78 0.23 2.73 0.02 0.04 0.24
fa10 0.74 0.74 0.79 0.24 2.79 0.02 0.04 0.24
kable(fa1$response.freq,
      caption = "Faith. Non missing response frequency for each item",
      label = 22, digits = 2)
Faith. Non missing response frequency for each item
0 1 2 3 4 5 miss
fa01 0.04 0.16 0.32 0.33 0.13 0.03 0
fa02 0.08 0.21 0.36 0.21 0.12 0.02 0
fa03 0.19 0.35 0.29 0.11 0.05 0.01 0
fa04 0.16 0.34 0.33 0.12 0.04 0.01 0
fa05 0.05 0.12 0.33 0.34 0.13 0.03 0
fa06 0.05 0.12 0.32 0.38 0.11 0.03 0
fa07 0.05 0.15 0.32 0.35 0.11 0.02 0
fa08 0.05 0.20 0.38 0.23 0.10 0.03 0
fa09 0.06 0.19 0.34 0.25 0.13 0.03 0
fa10 0.04 0.12 0.31 0.32 0.14 0.08 0

Dependability

de1 <- psych::alpha(
  taia %>% select(all_of(de_items_0)),
  cumulative = TRUE,
  title = "Dependability Factor",
  check.keys = FALSE
)
## Warning in psych::alpha(taia %>% select(all_of(de_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( de04 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
kable(de1$total,
      caption = "Dependability. Subscale statistics", 
      label = 23, digits = 2,
      col.names = total_colnames)
Dependability. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.75 0.75 0.8 0.21 2.96 0.02 28.09 6.74 0.24
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
      caption = "Dependability. Items statistics",
      label = 24, digits = 2,
      col.names = item_stats_colnames)
Dependability. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
de01 495 0.57 0.58 0.52 0.45 0.52 1.10
de02 495 0.72 0.72 0.69 0.61 0.43 1.15
de03 495 0.66 0.66 0.61 0.54 0.43 1.19
de04 495 -0.23 -0.22 -0.39 -0.36 0.38 1.05
de05 495 0.59 0.58 0.56 0.46 0.71 1.16
de06 495 0.67 0.67 0.62 0.55 0.45 1.23
de07 495 0.58 0.60 0.54 0.47 0.56 1.00
de08 495 0.67 0.68 0.66 0.57 0.53 1.06
de09 495 0.45 0.44 0.39 0.30 0.69 1.20
de10 495 0.74 0.74 0.72 0.64 0.45 1.18
de11 495 0.43 0.42 0.30 0.27 0.46 1.20
de1$item.stats %>%
  ggplot(aes(x = row.names(de1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Dependability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(de1$alpha.drop,
      caption = "Dependability. Subscale statistics when item drop",
      label = 25, digits = 2,
      col.names = alpha_drop_colnames)
Dependability. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
de01 0.73 0.72 0.78 0.21 2.59 0.02 0.07 0.22
de02 0.71 0.70 0.76 0.19 2.32 0.02 0.07 0.22
de03 0.72 0.71 0.77 0.20 2.44 0.02 0.07 0.23
de04 0.82 0.82 0.84 0.31 4.49 0.01 0.02 0.32
de05 0.73 0.72 0.76 0.21 2.59 0.02 0.07 0.22
de06 0.71 0.71 0.77 0.19 2.42 0.02 0.07 0.22
de07 0.73 0.72 0.78 0.20 2.56 0.02 0.07 0.22
de08 0.71 0.70 0.76 0.19 2.39 0.02 0.06 0.22
de09 0.75 0.74 0.78 0.22 2.88 0.02 0.07 0.30
de10 0.70 0.69 0.76 0.19 2.28 0.02 0.06 0.22
de11 0.75 0.75 0.80 0.23 2.93 0.02 0.08 0.32
kable(de1$response.freq,
      caption = "Dependability. Non missing response frequency for each item",
      label = 26, digits = 2)
Dependability. Non missing response frequency for each item
0 1 2 3 4 5 miss
de01 0.05 0.11 0.24 0.43 0.14 0.03 0
de02 0.09 0.17 0.36 0.26 0.10 0.02 0
de03 0.10 0.17 0.35 0.27 0.09 0.03 0
de04 0.07 0.26 0.45 0.14 0.05 0.02 0
de05 0.02 0.03 0.10 0.28 0.34 0.23 0
de06 0.10 0.17 0.32 0.28 0.11 0.03 0
de07 0.02 0.06 0.27 0.42 0.20 0.03 0
de08 0.04 0.10 0.24 0.44 0.15 0.03 0
de09 0.01 0.06 0.13 0.27 0.33 0.20 0
de10 0.10 0.15 0.30 0.34 0.10 0.02 0
de11 0.07 0.20 0.28 0.31 0.12 0.03 0

Understanding

un1 <- psych::alpha(
  taia %>% select(all_of(un_items_0)),
  cumulative = TRUE,
  title = "Understanding Factor",
  check.keys = FALSE
)
kable(un1$total,
      caption = "Understanding. Subscale statistics", 
      label = 27, digits = 2,
      col.names = total_colnames)
Understanding. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.92 0.92 0.92 0.5 12 0.01 31.24 10.15 0.51
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
      caption = "Understanding. Items statistics",
      label = 28, digits = 2,
      col.names = item_stats_colnames)
Understanding. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
un01 495 0.75 0.75 0.73 0.70 0.59 1.05
un02 495 0.84 0.84 0.84 0.81 0.49 1.14
un03 495 0.59 0.59 0.54 0.51 0.60 1.17
un04 495 0.76 0.76 0.73 0.71 0.52 1.09
un05 495 0.81 0.81 0.80 0.77 0.56 1.10
un06 495 0.57 0.56 0.50 0.48 0.46 1.23
un07 495 0.72 0.72 0.69 0.66 0.43 1.18
un08 495 0.76 0.76 0.75 0.71 0.58 1.16
un09 495 0.73 0.73 0.69 0.67 0.46 1.23
un10 495 0.75 0.75 0.72 0.69 0.45 1.15
un11 495 0.79 0.79 0.77 0.74 0.53 1.20
un12 495 0.75 0.76 0.73 0.70 0.58 1.12
un1$item.stats %>%
  ggplot(aes(x = row.names(un1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Understanding. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(un1$alpha.drop,
      caption = "Understanding. Subscale statistics when item drop",
      label = 29, digits = 2,
      col.names = alpha_drop_colnames)
Understanding. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
un01 0.91 0.92 0.91 0.50 10.87 0.01 0.01 0.51
un02 0.91 0.91 0.91 0.48 10.26 0.01 0.01 0.50
un03 0.92 0.92 0.92 0.52 12.05 0.01 0.01 0.54
un04 0.91 0.92 0.92 0.50 10.82 0.01 0.01 0.51
un05 0.91 0.91 0.91 0.49 10.46 0.01 0.01 0.50
un06 0.92 0.92 0.92 0.53 12.30 0.01 0.01 0.54
un07 0.92 0.92 0.92 0.50 11.10 0.01 0.01 0.51
un08 0.91 0.92 0.91 0.50 10.80 0.01 0.01 0.51
un09 0.92 0.92 0.92 0.50 11.06 0.01 0.01 0.51
un10 0.91 0.92 0.92 0.50 10.93 0.01 0.01 0.51
un11 0.91 0.91 0.91 0.49 10.62 0.01 0.01 0.50
un12 0.91 0.92 0.92 0.50 10.86 0.01 0.01 0.50
kable(un1$response.freq,
      caption = "Understanding. Non missing response frequency for each item",
      label = 30, digits = 2)
Understanding. Non missing response frequency for each item
0 1 2 3 4 5 miss
un01 0.02 0.07 0.19 0.43 0.24 0.05 0
un02 0.05 0.14 0.29 0.35 0.14 0.03 0
un03 0.03 0.08 0.16 0.36 0.29 0.07 0
un04 0.03 0.13 0.24 0.40 0.17 0.02 0
un05 0.04 0.08 0.19 0.44 0.21 0.04 0
un06 0.06 0.24 0.28 0.25 0.14 0.04 0
un07 0.09 0.21 0.30 0.29 0.09 0.02 0
un08 0.04 0.09 0.18 0.39 0.23 0.06 0
un09 0.08 0.18 0.25 0.30 0.17 0.01 0
un10 0.06 0.20 0.33 0.27 0.12 0.02 0
un11 0.05 0.13 0.23 0.36 0.18 0.05 0
un12 0.03 0.08 0.19 0.41 0.23 0.06 0

Split half reliabilitie

Predictability

splitHalf(taia %>% select(all_of(pr_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(pr_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.86
## Guttman lambda 6                          =  0.82
## Average split half reliability            =  0.81
## Guttman lambda 3 (alpha)                  =  0.81
## Guttman lambda 2                          =  0.82
## Minimum split half reliability  (beta)    =  0.74
## Average interitem r =  0.3  with median =  0.33

Consistency

splitHalf(taia %>% select(all_of(co_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(co_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.82
## Guttman lambda 6                          =  0.8
## Average split half reliability            =  0.77
## Guttman lambda 3 (alpha)                  =  0.77
## Guttman lambda 2                          =  0.8
## Minimum split half reliability  (beta)    =  0.66
## Average interitem r =  0.25  with median =  0.27

Utility

splitHalf(taia %>% select(all_of(ut_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(ut_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.9
## Guttman lambda 6                          =  0.87
## Average split half reliability            =  0.86
## Guttman lambda 3 (alpha)                  =  0.86
## Guttman lambda 2                          =  0.87
## Minimum split half reliability  (beta)    =  0.82
## Average interitem r =  0.34  with median =  0.37

Faith

splitHalf(taia %>% select(all_of(fa_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(fa_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.86
## Guttman lambda 6                          =  0.81
## Average split half reliability            =  0.77
## Guttman lambda 3 (alpha)                  =  0.77
## Guttman lambda 2                          =  0.79
## Minimum split half reliability  (beta)    =  0.56
## Average interitem r =  0.25  with median =  0.24

Dependability

splitHalf(taia %>% select(all_of(de_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(de_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.85
## Guttman lambda 6                          =  0.8
## Average split half reliability            =  0.74
## Guttman lambda 3 (alpha)                  =  0.75
## Guttman lambda 2                          =  0.78
## Minimum split half reliability  (beta)    =  0.52
## Average interitem r =  0.21  with median =  0.24

Understanding

splitHalf(taia %>% select(all_of(un_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(un_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.94
## Guttman lambda 6                          =  0.92
## Average split half reliability            =  0.92
## Guttman lambda 3 (alpha)                  =  0.92
## Guttman lambda 2                          =  0.92
## Minimum split half reliability  (beta)    =  0.89
## Average interitem r =  0.5  with median =  0.51

TAIA

splitHalf(taia %>% select(all_of(taia_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(taia_items_0)), raw = F, 
##     brute = F, n.sample = 100, covar = F, check.keys = F, key = NULL, 
##     use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.96
## Guttman lambda 6                          =  0.96
## Average split half reliability            =  0.93
## Guttman lambda 3 (alpha)                  =  0.93
## Guttman lambda 2                          =  0.94
## Minimum split half reliability  (beta)    =  0.83
## Average interitem r =  0.17  with median =  0.18

Items exclusion. First step

Excluded items: co07, ut10, de04

Reason: negative discrimination

Stems:

  • co07 Я могу сказать, что система работает корректно, только протестировав её (R)
  • ut10 Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
  • de04 Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё (R)
pr_items_1 <- pr_items_0
co_items_1 <- co_items_0[-7]
ut_items_1 <- ut_items_0[-10]
fa_items_1 <- fa_items_0
de_items_1 <- de_items_0[-4]
un_items_1 <- un_items_0
taia_items_1 <- c(pr_items_1,
                  co_items_1,
                  ut_items_1,
                  fa_items_1,
                  de_items_1,
                  un_items_1)

Subscales after first step of exclusion

Consistency

co2 <- psych::alpha(
  taia %>% select(all_of(co_items_1)),
  cumulative = TRUE,
  title = "Consistency Factor",
  check.keys = FALSE
)
kable(co2$total,
      caption = "Consistency. Subscale statistics", 
      label = 11, digits = 2,
      col.names = total_colnames)
Consistency. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.83 0.82 0.83 0.34 4.7 0.01 22.44 6.24 0.36
co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
      caption = "Consistency. Items statistics",
      label = 12, digits = 2,
      col.names = item_stats_colnames)
Consistency. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
co01 495 0.74 0.74 0.71 0.65 0.50 1.08
co02 495 0.72 0.72 0.67 0.62 0.50 1.04
co03 495 0.56 0.57 0.48 0.43 0.57 1.02
co04 495 0.44 0.43 0.33 0.28 0.69 1.09
co05 495 0.79 0.78 0.78 0.70 0.44 1.11
co06 495 0.68 0.67 0.62 0.56 0.50 1.11
co08 495 0.44 0.44 0.34 0.29 0.38 1.04
co09 495 0.76 0.76 0.75 0.67 0.41 1.07
co10 495 0.68 0.68 0.62 0.57 0.49 1.10
co2$item.stats %>%
  ggplot(aes(x = row.names(co2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Consistency. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(co2$alpha.drop,
      caption = "Consistency. Subscale statistics when item drop",
      label = 13, digits = 2,
      col.names = alpha_drop_colnames)
Consistency. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
co01 0.79 0.79 0.80 0.32 3.81 0.01 0.03 0.34
co02 0.80 0.80 0.81 0.33 3.89 0.01 0.03 0.35
co03 0.82 0.82 0.82 0.36 4.49 0.01 0.03 0.41
co04 0.84 0.83 0.83 0.39 5.04 0.01 0.02 0.41
co05 0.79 0.79 0.79 0.31 3.66 0.01 0.02 0.33
co06 0.80 0.80 0.81 0.34 4.06 0.01 0.03 0.35
co08 0.83 0.83 0.83 0.39 5.01 0.01 0.02 0.40
co09 0.79 0.79 0.80 0.32 3.74 0.01 0.02 0.34
co10 0.80 0.80 0.81 0.34 4.04 0.01 0.03 0.35
kable(co2$response.freq,
      caption = "Consistency. Non missing response frequency for each item",
      label = 14, digits = 2)
Consistency. Non missing response frequency for each item
0 1 2 3 4 5 miss
co01 0.05 0.11 0.31 0.39 0.11 0.03 0
co02 0.03 0.12 0.32 0.38 0.13 0.02 0
co03 0.02 0.07 0.22 0.44 0.21 0.04 0
co04 0.01 0.04 0.10 0.35 0.32 0.18 0
co05 0.06 0.19 0.37 0.27 0.09 0.02 0
co06 0.04 0.14 0.28 0.38 0.13 0.03 0
co08 0.08 0.27 0.42 0.16 0.06 0.01 0
co09 0.06 0.24 0.40 0.22 0.06 0.02 0
co10 0.04 0.14 0.33 0.34 0.11 0.03 0

Utility

ut2 <- psych::alpha(
  taia %>% select(all_of(ut_items_1)),
  cumulative = TRUE,
  title = "Utility Factor",
  check.keys = FALSE
)
kable(ut2$total,
      caption = "Utility. Subscale statistics", 
      label = 15, digits = 2,
      col.names = total_colnames)
Utility. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.88 0.88 0.88 0.4 7.4 0.01 35.92 8.32 0.41
ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
      caption = "Utility. Items statistics",
      label = 16, digits = 2,
      col.names = item_stats_colnames)
Utility. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
ut01 495 0.79 0.79 0.79 0.73 0.76 1.05
ut02 495 0.83 0.84 0.84 0.79 0.70 1.05
ut03 495 0.55 0.56 0.49 0.45 0.71 1.11
ut04 495 0.53 0.53 0.46 0.42 0.62 1.11
ut05 495 0.69 0.68 0.64 0.60 0.61 1.21
ut06 495 0.77 0.77 0.75 0.71 0.65 1.10
ut07 495 0.62 0.62 0.57 0.52 0.64 1.13
ut08 495 0.66 0.67 0.62 0.58 0.69 1.05
ut09 495 0.70 0.70 0.66 0.62 0.64 1.17
ut11 495 0.57 0.56 0.49 0.46 0.53 1.23
ut12 495 0.72 0.72 0.68 0.65 0.63 1.15
ut2$item.stats %>%
  ggplot(aes(x = row.names(ut2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Utility. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(ut2$alpha.drop,
      caption = "Utility. Subscale statistics when item drop",
      label = 17, digits = 2,
      col.names = alpha_drop_colnames)
Utility. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
ut01 0.86 0.86 0.86 0.38 6.21 0.01 0.01 0.40
ut02 0.86 0.86 0.86 0.38 6.01 0.01 0.01 0.37
ut03 0.88 0.88 0.88 0.42 7.30 0.01 0.01 0.43
ut04 0.88 0.88 0.88 0.43 7.41 0.01 0.01 0.43
ut05 0.87 0.87 0.87 0.40 6.69 0.01 0.01 0.41
ut06 0.86 0.86 0.87 0.39 6.30 0.01 0.01 0.41
ut07 0.87 0.87 0.87 0.41 6.99 0.01 0.01 0.42
ut08 0.87 0.87 0.87 0.40 6.77 0.01 0.01 0.41
ut09 0.87 0.87 0.87 0.40 6.64 0.01 0.02 0.41
ut11 0.88 0.88 0.88 0.42 7.27 0.01 0.01 0.44
ut12 0.86 0.87 0.87 0.39 6.52 0.01 0.02 0.41
kable(ut2$response.freq,
      caption = "Utility. Non missing response frequency for each item",
      label = 18, digits = 2)
Utility. Non missing response frequency for each item
0 1 2 3 4 5 miss
ut01 0.01 0.01 0.06 0.29 0.34 0.28 0
ut02 0.01 0.02 0.09 0.38 0.30 0.20 0
ut03 0.01 0.04 0.09 0.33 0.31 0.22 0
ut04 0.02 0.08 0.15 0.40 0.27 0.09 0
ut05 0.03 0.06 0.20 0.35 0.23 0.12 0
ut06 0.03 0.03 0.13 0.39 0.30 0.12 0
ut07 0.01 0.05 0.19 0.35 0.26 0.14 0
ut08 0.01 0.03 0.11 0.36 0.34 0.15 0
ut09 0.03 0.05 0.15 0.38 0.25 0.13 0
ut11 0.05 0.12 0.27 0.32 0.18 0.07 0
ut12 0.02 0.07 0.15 0.38 0.26 0.12 0

Dependability

de2 <- psych::alpha(
  taia %>% select(all_of(de_items_1)),
  cumulative = TRUE,
  title = "Dependability Factor",
  check.keys = FALSE
)
kable(de2$total,
      caption = "Dependability. Subscale statistics", 
      label = 23, digits = 2,
      col.names = total_colnames)
Dependability. Subscale statistics
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Scale Mean Total Score SD Median interitem correlation
0.82 0.82 0.84 0.31 4.49 0.01 26.19 7.05 0.32
de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
      caption = "Dependability. Items statistics",
      label = 24, digits = 2,
      col.names = item_stats_colnames)
Dependability. Items statistics
Num. of Obs. Discrimination Std Cor Cor Overlap Corrected Cor if drop Difficulty SD
de01 495 0.58 0.59 0.53 0.47 0.52 1.10
de02 495 0.72 0.72 0.68 0.62 0.43 1.15
de03 495 0.65 0.65 0.60 0.54 0.43 1.19
de05 495 0.62 0.62 0.59 0.50 0.71 1.16
de06 495 0.67 0.67 0.62 0.56 0.45 1.23
de07 495 0.61 0.62 0.56 0.51 0.56 1.00
de08 495 0.70 0.71 0.67 0.61 0.53 1.06
de09 495 0.46 0.45 0.40 0.31 0.69 1.20
de10 495 0.73 0.73 0.71 0.64 0.45 1.18
de11 495 0.41 0.40 0.28 0.25 0.46 1.20
de2$item.stats %>%
  ggplot(aes(x = row.names(de2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Dependability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(de2$alpha.drop,
      caption = "Dependability. Subscale statistics when item drop",
      label = 25, digits = 2,
      col.names = alpha_drop_colnames)
Dependability. Subscale statistics when item drop
Alpha Standardized Alpha Guttman’s Lambda 6 Average interitem correlation S/N Alpha SE Var(r) Median interitem correlation
de01 0.80 0.80 0.82 0.31 4.11 0.01 0.02 0.31
de02 0.79 0.79 0.81 0.29 3.72 0.01 0.02 0.29
de03 0.79 0.80 0.82 0.30 3.93 0.01 0.02 0.29
de05 0.80 0.80 0.80 0.31 4.04 0.01 0.02 0.35
de06 0.79 0.80 0.81 0.30 3.88 0.01 0.02 0.31
de07 0.80 0.80 0.82 0.31 4.02 0.01 0.02 0.29
de08 0.79 0.79 0.81 0.29 3.75 0.01 0.02 0.29
de09 0.82 0.82 0.82 0.34 4.60 0.01 0.02 0.35
de10 0.78 0.79 0.80 0.29 3.67 0.01 0.02 0.29
de11 0.83 0.83 0.84 0.35 4.79 0.01 0.02 0.37
kable(de2$response.freq,
      caption = "Dependability. Non missing response frequency for each item",
      label = 26, digits = 2)
Dependability. Non missing response frequency for each item
0 1 2 3 4 5 miss
de01 0.05 0.11 0.24 0.43 0.14 0.03 0
de02 0.09 0.17 0.36 0.26 0.10 0.02 0
de03 0.10 0.17 0.35 0.27 0.09 0.03 0
de05 0.02 0.03 0.10 0.28 0.34 0.23 0
de06 0.10 0.17 0.32 0.28 0.11 0.03 0
de07 0.02 0.06 0.27 0.42 0.20 0.03 0
de08 0.04 0.10 0.24 0.44 0.15 0.03 0
de09 0.01 0.06 0.13 0.27 0.33 0.20 0
de10 0.10 0.15 0.30 0.34 0.10 0.02 0
de11 0.07 0.20 0.28 0.31 0.12 0.03 0

Reliability measures

Consistency

splitHalf(taia %>% select(all_of(co_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(co_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.87
## Guttman lambda 6                          =  0.83
## Average split half reliability            =  0.82
## Guttman lambda 3 (alpha)                  =  0.82
## Guttman lambda 2                          =  0.83
## Minimum split half reliability  (beta)    =  0.72
## Average interitem r =  0.34  with median =  0.36

Utility

splitHalf(taia %>% select(all_of(ut_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(ut_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.91
## Guttman lambda 6                          =  0.88
## Average split half reliability            =  0.87
## Guttman lambda 3 (alpha)                  =  0.88
## Guttman lambda 2                          =  0.88
## Minimum split half reliability  (beta)    =  0.83
## Average interitem r =  0.4  with median =  0.41

Dependability

splitHalf(taia %>% select(all_of(de_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(de_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.88
## Guttman lambda 6                          =  0.84
## Average split half reliability            =  0.82
## Guttman lambda 3 (alpha)                  =  0.82
## Guttman lambda 2                          =  0.83
## Minimum split half reliability  (beta)    =  0.72
## Average interitem r =  0.31  with median =  0.32

TAIA

splitHalf(taia %>% ungroup() %>% select(all_of(taia_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")
## Split half reliabilities  
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items_1)), 
##     raw = F, brute = F, n.sample = 100, covar = F, check.keys = F, 
##     key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.96
## Guttman lambda 6                          =  0.97
## Average split half reliability            =  0.94
## Guttman lambda 3 (alpha)                  =  0.94
## Guttman lambda 2                          =  0.94
## Minimum split half reliability  (beta)    =  0.89
## Average interitem r =  0.2  with median =  0.2

Exploratory Factor Analysis

6 factors

varimax rotation

efa_6f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 6,
  scores = "regression",
  rotation = "varimax"
)
loadings(efa_6f_vm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01  0.489   0.475   0.172           0.287         
## pr02  0.263   0.392   0.219           0.164   0.181 
## pr03  0.219                           0.547         
## pr04         -0.109           0.139   0.437         
## pr05  0.204   0.350   0.139           0.123   0.676 
## pr06  0.475   0.279   0.144                   0.105 
## pr07  0.387   0.525                   0.208   0.134 
## pr08  0.492   0.429   0.125           0.241         
## pr09  0.350   0.347   0.133           0.220         
## pr10  0.328   0.301   0.124           0.108         
## co01  0.243   0.676           0.116                 
## co02  0.169   0.626   0.115                         
## co03  0.416   0.370                   0.159   0.141 
## co04  0.532   0.147   0.121           0.161         
## co05  0.137   0.724                                 
## co06  0.152   0.554          -0.117                 
## co08 -0.118   0.413  -0.115  -0.127  -0.467         
## co09          0.676                  -0.160         
## co10  0.184   0.526   0.206          -0.123         
## ut01  0.810                                         
## ut02  0.806           0.102           0.135   0.125 
## ut03  0.472  -0.102           0.119   0.507         
## ut04  0.461                   0.104           0.132 
## ut05  0.598   0.194                                 
## ut06  0.717   0.164                           0.125 
## ut07  0.550   0.207                                 
## ut08  0.576   0.249                                 
## ut09  0.593   0.174                   0.102   0.128 
## ut11  0.368   0.255   0.158           0.126   0.591 
## ut12  0.604   0.233   0.135           0.142         
## fa01  0.300   0.345           0.628                 
## fa02                 -0.207   0.669   0.148         
## fa03 -0.116   0.385                  -0.318   0.148 
## fa04          0.549           0.103  -0.192   0.177 
## fa05  0.293   0.411           0.622                 
## fa06  0.301   0.573   0.126   0.188   0.182   0.163 
## fa07                          0.196   0.474   0.193 
## fa08                 -0.191   0.602   0.232         
## fa09                 -0.155   0.631   0.310         
## fa10  0.280   0.136           0.599                 
## de01  0.278   0.429   0.162                         
## de02  0.225   0.571   0.129           0.155   0.228 
## de03  0.223   0.475   0.115   0.138           0.214 
## de05  0.508   0.173   0.137           0.385         
## de06  0.218   0.352   0.184   0.171           0.666 
## de07  0.439   0.301   0.220                   0.182 
## de08  0.345   0.412   0.193   0.107   0.185   0.205 
## de09  0.259                           0.592         
## de10  0.287   0.512           0.177           0.351 
## de11                          0.181   0.332   0.255 
## un01  0.287           0.731                         
## un02          0.138   0.815                         
## un03  0.207           0.502  -0.247                 
## un04          0.113   0.711                         
## un05  0.191           0.791                         
## un06 -0.131           0.518           0.189         
## un07          0.286   0.648          -0.139   0.128 
## un08  0.182           0.746                         
## un09          0.162   0.662                   0.197 
## un10          0.261   0.690                         
## un11                  0.763                         
## un12  0.236           0.713                         
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings      7.605   7.077   6.534   2.832   2.786   2.029
## Proportion Var   0.123   0.114   0.105   0.046   0.045   0.033
## Cumulative Var   0.123   0.237   0.342   0.388   0.433   0.466
efa_6f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)
Uniqueness
de11 0.78
pr04 0.77
pr10 0.76
ut04 0.76
fa03 0.71
de01 0.70
fa07 0.69
pr09 0.69
un06 0.68
pr02 0.67
pr06 0.66
ut07 0.65
co04 0.65
co06 0.65
de03 0.65
un03 0.64
pr03 0.64
co03 0.63
co10 0.63
de07 0.62
fa04 0.61
ut08 0.59
ut05 0.59
ut09 0.59
de08 0.59
de09 0.58
co08 0.57
co02 0.56
de05 0.54
fa10 0.54
ut12 0.54
fa08 0.53
de02 0.52
co09 0.51
pr07 0.50
pr08 0.50
ut03 0.49
de10 0.49
un09 0.49
fa09 0.47
fa02 0.47
fa06 0.47
un04 0.46
co01 0.46
un07 0.46
co05 0.45
ut06 0.44
un10 0.44
un12 0.43
pr01 0.41
ut11 0.41
un08 0.41
un11 0.39
fa01 0.38
un01 0.37
fa05 0.35
pr05 0.34
ut01 0.33
un05 0.33
de06 0.31
un02 0.31
ut02 0.30

promax rotation

efa_6f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 6,
  scores = "regression",
  rotation = "promax"
)
loadings(efa_6f_pm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01  0.461           0.225   0.283                 
## pr02  0.338   0.110           0.148           0.136 
## pr03         -0.110           0.614                 
## pr04 -0.141                   0.484                 
## pr05                                          0.790 
## pr06  0.174           0.410                         
## pr07  0.527           0.131   0.192                 
## pr08  0.422           0.278   0.238                 
## pr09  0.361           0.146   0.227                 
## pr10  0.263           0.204          -0.122         
## co01  0.809                           0.102  -0.145 
## co02  0.765                                  -0.137 
## co03  0.303           0.264   0.114                 
## co04                  0.493   0.121          -0.132 
## co05  0.902  -0.102  -0.117                  -0.109 
## co06  0.668                          -0.154         
## co08  0.542  -0.180          -0.501  -0.100         
## co09  0.817  -0.113  -0.123  -0.169                 
## co10  0.598   0.115          -0.135                 
## ut01 -0.249           0.974                         
## ut02 -0.231           0.910                   0.113 
## ut03 -0.213           0.385   0.515                 
## ut04 -0.198           0.552                   0.146 
## ut05                  0.658  -0.124                 
## ut06                  0.789                   0.108 
## ut07  0.128           0.593                         
## ut08  0.186           0.543                  -0.108 
## ut09                  0.608                   0.110 
## ut11                  0.291                   0.694 
## ut12  0.106           0.559                         
## fa01  0.277   0.125   0.171           0.650         
## fa02                 -0.129           0.695         
## fa03  0.418          -0.154  -0.379           0.158 
## fa04  0.609          -0.225  -0.241           0.146 
## fa05  0.367   0.105   0.156  -0.119   0.648         
## fa06  0.580                   0.149   0.131         
## fa07                 -0.218   0.508   0.109   0.173 
## fa08                 -0.179   0.165   0.604         
## fa09                 -0.187   0.254   0.627         
## fa10                  0.281  -0.127   0.648  -0.119 
## de01  0.460                                         
## de02  0.569                   0.135           0.166 
## de03  0.430                                   0.173 
## de05  0.114           0.340   0.407  -0.107         
## de06                                          0.770 
## de07  0.160   0.109   0.337                   0.156 
## de08  0.329           0.129   0.141           0.153 
## de09                          0.692  -0.101         
## de10  0.397           0.106           0.103   0.339 
## de11         -0.106  -0.103   0.320   0.101   0.273 
## un01 -0.148   0.804   0.225           0.123  -0.141 
## un02          0.857  -0.105                         
## un03          0.451   0.145          -0.247         
## un04          0.734                                 
## un05          0.844                                 
## un06          0.556  -0.347   0.256                 
## un07  0.192   0.641  -0.115  -0.172           0.113 
## un08 -0.114   0.817                   0.113         
## un09          0.646  -0.101                   0.212 
## un10  0.219   0.698  -0.212                         
## un11          0.803          -0.103                 
## un12 -0.180   0.740   0.166                         
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings      7.535   6.547   6.389   3.041   2.823   2.419
## Proportion Var   0.122   0.106   0.103   0.049   0.046   0.039
## Cumulative Var   0.122   0.227   0.330   0.379   0.425   0.464
efa_6f_pm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)
Uniqueness
de11 0.78
pr04 0.77
pr10 0.76
ut04 0.76
fa03 0.71
de01 0.70
fa07 0.69
pr09 0.69
un06 0.68
pr02 0.67
pr06 0.66
ut07 0.65
co04 0.65
co06 0.65
de03 0.65
un03 0.64
pr03 0.64
co03 0.63
co10 0.63
de07 0.62
fa04 0.61
ut08 0.59
ut05 0.59
ut09 0.59
de08 0.59
de09 0.58
co08 0.57
co02 0.56
de05 0.54
fa10 0.54
ut12 0.54
fa08 0.53
de02 0.52
co09 0.51
pr07 0.50
pr08 0.50
ut03 0.49
de10 0.49
un09 0.49
fa09 0.47
fa02 0.47
fa06 0.47
un04 0.46
co01 0.46
un07 0.46
co05 0.45
ut06 0.44
un10 0.44
un12 0.43
pr01 0.41
ut11 0.41
un08 0.41
un11 0.39
fa01 0.38
un01 0.37
fa05 0.35
pr05 0.34
ut01 0.33
un05 0.33
de06 0.31
un02 0.31
ut02 0.30

5 factors

varimax rotation

efa_5f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 5,
  scores = "regression",
  rotation = "varimax"
)
loadings(efa_5f_vm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5
## pr01  0.576   0.372   0.170   0.212         
## pr02  0.319   0.359   0.221   0.205         
## pr03  0.306  -0.108           0.412   0.121 
## pr04  0.131  -0.197           0.340   0.154 
## pr05  0.211   0.453   0.156   0.482         
## pr06  0.501   0.249   0.142   0.100         
## pr07  0.456   0.465           0.232         
## pr08  0.567   0.334   0.123   0.160         
## pr09  0.424   0.257   0.129   0.129         
## pr10  0.369   0.262   0.124   0.104         
## co01  0.303   0.625                   0.130 
## co02  0.235   0.573   0.114           0.112 
## co03  0.462   0.329           0.176         
## co04  0.573           0.115                 
## co05  0.197   0.690                         
## co06  0.207   0.520                         
## co08 -0.157   0.505  -0.112  -0.326  -0.146 
## co09  0.121   0.681          -0.100         
## co10  0.219   0.513   0.206  -0.125         
## ut01  0.788                                 
## ut02  0.792           0.100   0.136         
## ut03  0.531  -0.225           0.358   0.140 
## ut04  0.437                   0.115         
## ut05  0.586   0.191                         
## ut06  0.713   0.148                         
## ut07  0.559   0.171                         
## ut08  0.612   0.179                         
## ut09  0.601   0.152           0.128         
## ut11  0.362   0.341   0.171   0.431         
## ut12  0.632   0.179   0.131   0.115         
## fa01  0.317   0.338           0.123   0.618 
## fa02                 -0.206   0.202   0.659 
## fa03 -0.148   0.484          -0.103         
## fa04          0.616                         
## fa05  0.305   0.412                   0.604 
## fa06  0.366   0.528   0.127   0.241   0.179 
## fa07                          0.512   0.182 
## fa08                 -0.190   0.284   0.591 
## fa09                 -0.155   0.321   0.632 
## fa10  0.278   0.126                   0.611 
## de01  0.320   0.386   0.159                 
## de02  0.282   0.553   0.132   0.281         
## de03  0.246   0.493   0.117   0.176   0.105 
## de05  0.583           0.133   0.230         
## de06  0.219   0.462   0.197   0.470         
## de07  0.462   0.290   0.220   0.182         
## de08  0.392   0.386   0.195   0.273         
## de09  0.367                   0.374         
## de10  0.300   0.558           0.303   0.121 
## de11                          0.445   0.153 
## un01  0.301           0.727                 
## un02          0.124   0.815                 
## un03  0.221           0.501          -0.243 
## un04  0.107   0.116   0.712                 
## un05  0.219           0.787                 
## un06                  0.519   0.148         
## un07          0.328   0.651                 
## un08  0.195           0.744                 
## un09          0.198   0.667   0.124  -0.110 
## un10          0.272   0.692          -0.101 
## un11          0.107   0.764                 
## un12  0.242           0.712                 
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings      8.695   6.882   6.542   2.836   2.744
## Proportion Var   0.140   0.111   0.106   0.046   0.044
## Cumulative Var   0.140   0.251   0.357   0.403   0.447
efa_5f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)
Uniqueness
pr04 0.80
ut04 0.79
de11 0.77
pr10 0.76
fa03 0.73
pr09 0.72
de09 0.71
de01 0.71
pr03 0.71
un06 0.70
fa07 0.69
pr02 0.68
co06 0.67
pr06 0.66
ut07 0.65
co04 0.64
de03 0.64
un03 0.64
co03 0.63
co10 0.63
de07 0.62
ut05 0.61
fa04 0.61
ut09 0.60
co02 0.59
ut08 0.59
de05 0.59
co08 0.58
de08 0.58
ut12 0.54
ut11 0.54
fa10 0.53
fa08 0.53
pr08 0.53
ut03 0.52
de02 0.52
co09 0.51
pr07 0.51
co01 0.49
pr05 0.49
de10 0.49
un09 0.49
fa06 0.48
co05 0.47
fa09 0.47
fa02 0.47
de06 0.47
un04 0.46
un07 0.46
ut06 0.46
pr01 0.45
un10 0.44
un12 0.43
un08 0.40
un11 0.40
fa01 0.39
ut01 0.37
un01 0.37
fa05 0.36
ut02 0.34
un05 0.33
un02 0.31

promax rotation

efa_5f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 5,
  scores = "regression",
  rotation = "promax"
)
loadings(efa_5f_pm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5
## pr01  0.446   0.279           0.163         
## pr02  0.143   0.296   0.114   0.211         
## pr03  0.233  -0.214  -0.117   0.424         
## pr04         -0.293           0.354   0.107 
## pr05 -0.115   0.392           0.579         
## pr06  0.456   0.170                         
## pr07  0.281   0.412           0.218         
## pr08  0.480   0.250           0.106         
## pr09  0.342   0.189                         
## pr10  0.300   0.206                  -0.112 
## co01  0.146   0.650                   0.122 
## co02          0.599                   0.111 
## co03  0.354   0.265           0.139         
## co04  0.624                                 
## co05          0.759          -0.112         
## co06          0.545                  -0.122 
## co08 -0.181   0.655  -0.178  -0.313  -0.109 
## co09          0.763  -0.105  -0.100         
## co10  0.114   0.531   0.122  -0.157         
## ut01  0.932  -0.173          -0.161         
## ut02  0.859  -0.129                         
## ut03  0.556  -0.375           0.303         
## ut04  0.452                                 
## ut05  0.610   0.111                         
## ut06  0.757                                 
## ut07  0.631   0.114          -0.191         
## ut08  0.655                  -0.102         
## ut09  0.605                                 
## ut11  0.121   0.247           0.486         
## ut12  0.625                                 
## fa01  0.152   0.310   0.125           0.634 
## fa02 -0.203                   0.143   0.681 
## fa03 -0.291   0.581                         
## fa04 -0.281   0.695                         
## fa05  0.129   0.400                   0.620 
## fa06  0.134   0.490           0.229   0.130 
## fa07 -0.104                   0.583   0.102 
## fa08 -0.210                   0.254   0.592 
## fa09 -0.158                   0.280   0.627 
## fa10  0.247   0.110          -0.175   0.648 
## de01  0.200   0.356                         
## de02          0.524           0.319         
## de03          0.478           0.183         
## de05  0.562                   0.179         
## de06 -0.118   0.396           0.550         
## de07  0.346   0.199   0.104   0.156         
## de08  0.192   0.310           0.272         
## de09  0.305  -0.213           0.381         
## de10          0.534           0.332         
## de11                 -0.110   0.509         
## un01  0.258  -0.197   0.810  -0.169   0.132 
## un02                  0.857                 
## un03  0.185           0.450          -0.246 
## un04                  0.739                 
## un05  0.103  -0.121   0.847                 
## un06 -0.250  -0.119   0.556   0.200         
## un07 -0.175   0.266   0.641                 
## un08  0.101  -0.128   0.827  -0.111   0.126 
## un09 -0.166           0.647   0.170         
## un10 -0.202   0.204   0.699                 
## un11                  0.805                 
## un12  0.151  -0.155   0.746                 
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings      7.635   6.936   6.600   3.174   2.713
## Proportion Var   0.123   0.112   0.106   0.051   0.044
## Cumulative Var   0.123   0.235   0.341   0.393   0.436
efa_5f_pm$uniquenesses %>%
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness")
Uniqueness
pr04 0.8036037
ut04 0.7875168
de11 0.7660851
pr10 0.7648954
fa03 0.7322846
pr09 0.7157810
de09 0.7138430
de01 0.7115636
pr03 0.7071547
un06 0.6984492
fa07 0.6934404
pr02 0.6780781
co06 0.6691958
pr06 0.6557484
ut07 0.6500545
co04 0.6435044
de03 0.6411034
un03 0.6404520
co03 0.6336224
co10 0.6291329
de07 0.6207641
ut05 0.6108805
fa04 0.6096628
ut09 0.5950296
co02 0.5895055
ut08 0.5880908
de05 0.5859234
co08 0.5800904
de08 0.5776240
ut12 0.5381209
ut11 0.5378917
fa10 0.5312032
fa08 0.5256412
pr08 0.5255447
ut03 0.5181711
de02 0.5158859
co09 0.5114014
pr07 0.5096638
co01 0.4937745
pr05 0.4926627
de10 0.4869821
un09 0.4861097
fa06 0.4806029
co05 0.4747549
fa09 0.4734086
fa02 0.4713133
de06 0.4691654
un04 0.4646410
un07 0.4595727
ut06 0.4594009
pr01 0.4496805
un10 0.4361787
un12 0.4325425
un08 0.4008023
un11 0.3971241
fa01 0.3854741
ut01 0.3691461
un01 0.3671937
fa05 0.3613714
ut02 0.3407660
un05 0.3303704
un02 0.3106689

4 factors

varimax rotation

efa_4f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 4,
  scores = "regression",
  rotation = "varimax"
)
loadings(efa_4f_vm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4
## pr01  0.587   0.391   0.173   0.154 
## pr02  0.338   0.367   0.225   0.105 
## pr03  0.355                   0.326 
## pr04  0.172  -0.184           0.315 
## pr05  0.275   0.444   0.169   0.249 
## pr06  0.508   0.257   0.143         
## pr07  0.470   0.479           0.161 
## pr08  0.574   0.350   0.123         
## pr09  0.428   0.271   0.129   0.115 
## pr10  0.379   0.267   0.125         
## co01  0.271   0.643                 
## co02  0.204   0.589   0.107         
## co03  0.470   0.344           0.135 
## co04  0.557           0.111         
## co05  0.162   0.700                 
## co06  0.195   0.523          -0.117 
## co08 -0.202   0.492  -0.128  -0.316 
## co09          0.686                 
## co10  0.187   0.519   0.196  -0.125 
## ut01  0.767                         
## ut02  0.795           0.102   0.101 
## ut03  0.569  -0.204           0.312 
## ut04  0.442                   0.118 
## ut05  0.572   0.207                 
## ut06  0.712   0.163                 
## ut07  0.530   0.188                 
## ut08  0.595   0.198                 
## ut09  0.609   0.166                 
## ut11  0.418   0.338   0.182   0.195 
## ut12  0.634   0.196   0.132         
## fa01  0.278   0.374           0.546 
## fa02 -0.114          -0.197   0.674 
## fa03 -0.169   0.479                 
## fa04          0.618                 
## fa05  0.263   0.446           0.521 
## fa06  0.374   0.545   0.131   0.248 
## fa07  0.158                   0.427 
## fa08                 -0.178   0.669 
## fa09                 -0.141   0.719 
## fa10  0.220   0.166           0.481 
## de01  0.308   0.402   0.157         
## de02  0.307   0.559   0.139   0.167 
## de03  0.252   0.503   0.119   0.155 
## de05  0.609           0.140   0.109 
## de06  0.276   0.458   0.209   0.295 
## de07  0.479   0.300   0.223         
## de08  0.415   0.398   0.200   0.194 
## de09  0.414                   0.238 
## de10  0.325   0.565           0.232 
## de11  0.139                   0.366 
## un01  0.278           0.716         
## un02          0.132   0.815         
## un03  0.236           0.501  -0.225 
## un04  0.105   0.121   0.710         
## un05  0.211           0.785         
## un06                  0.527         
## un07          0.328   0.648  -0.110 
## un08  0.178           0.737         
## un09          0.195   0.669         
## un10          0.272   0.690  -0.127 
## un11          0.110   0.762         
## un12  0.244           0.712         
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      8.889   7.151   6.515   3.709
## Proportion Var   0.143   0.115   0.105   0.060
## Cumulative Var   0.143   0.259   0.364   0.424
efa_4f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)
Uniqueness
de11 0.84
pr04 0.83
fa07 0.79
ut04 0.79
pr10 0.77
de09 0.76
pr03 0.76
fa03 0.74
un06 0.72
pr09 0.71
de01 0.71
fa10 0.69
pr02 0.69
ut07 0.68
co06 0.67
co04 0.66
pr06 0.65
de03 0.65
un03 0.64
co10 0.64
ut11 0.64
pr05 0.64
co03 0.63
de07 0.63
ut05 0.62
fa04 0.61
ut08 0.60
co08 0.60
co02 0.60
ut09 0.59
de05 0.59
de08 0.59
de06 0.58
de02 0.55
ut12 0.54
ut03 0.54
pr08 0.52
de10 0.52
pr07 0.52
fa08 0.51
co09 0.51
un09 0.51
co01 0.50
fa02 0.49
fa06 0.48
fa01 0.48
co05 0.48
un04 0.46
fa09 0.46
ut06 0.46
fa05 0.46
un07 0.46
pr01 0.45
un10 0.43
un12 0.43
un08 0.42
un01 0.41
ut01 0.40
un11 0.40
ut02 0.35
un05 0.34
un02 0.31

promax rotation

efa_4f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 4,
  scores = "regression",
  rotation = "promax"
)
loadings(efa_4f_pm)
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4
## pr01  0.515   0.294                 
## pr02  0.217   0.309   0.150         
## pr03  0.371  -0.195           0.265 
## pr04  0.154  -0.280           0.308 
## pr05          0.406   0.109   0.225 
## pr06  0.516   0.174          -0.122 
## pr07  0.359   0.431                 
## pr08  0.543   0.264                 
## pr09  0.378   0.198                 
## pr10  0.363   0.214          -0.107 
## co01          0.667                 
## co02          0.614                 
## co03  0.411   0.279                 
## co04  0.611                         
## co05          0.774  -0.122         
## co06          0.559          -0.170 
## co08 -0.270   0.651  -0.216  -0.301 
## co09          0.777  -0.122  -0.111 
## co10          0.537          -0.169 
## ut01  0.933  -0.176          -0.188 
## ut02  0.916  -0.128                 
## ut03  0.673  -0.364           0.189 
## ut04  0.489                         
## ut05  0.618   0.113          -0.120 
## ut06  0.809                  -0.146 
## ut07  0.599   0.112  -0.103  -0.159 
## ut08  0.656   0.103          -0.129 
## ut09  0.672                  -0.103 
## ut11  0.306   0.261   0.104   0.124 
## ut12  0.674                         
## fa01          0.321           0.549 
## fa02 -0.314          -0.103   0.777 
## fa03 -0.335   0.587                 
## fa04 -0.302   0.710                 
## fa05          0.410           0.525 
## fa06  0.181   0.511           0.195 
## fa07                          0.437 
## fa08 -0.273                   0.764 
## fa09 -0.219                   0.803 
## fa10          0.116           0.481 
## de01  0.189   0.368                 
## de02  0.118   0.544           0.124 
## de03          0.495           0.123 
## de05  0.665                         
## de06          0.411   0.159   0.280 
## de07  0.425   0.209   0.126         
## de08  0.278   0.327   0.116   0.126 
## de09  0.447  -0.196           0.156 
## de10  0.131   0.553           0.188 
## de11                          0.372 
## un01  0.179  -0.198   0.750         
## un02 -0.107           0.869         
## un03  0.243           0.480  -0.283 
## un04                  0.745         
## un05         -0.122   0.829         
## un06 -0.214  -0.110   0.611   0.106 
## un07 -0.189   0.270   0.659         
## un08         -0.130   0.788         
## un09 -0.110           0.698         
## un10 -0.225   0.207   0.718         
## un11 -0.115           0.813         
## un12  0.142  -0.159   0.744         
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      9.044   7.246   6.725   3.937
## Proportion Var   0.146   0.117   0.108   0.064
## Cumulative Var   0.146   0.263   0.371   0.435
efa_4f_pm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)
Uniqueness
de11 0.84
pr04 0.83
fa07 0.79
ut04 0.79
pr10 0.77
de09 0.76
pr03 0.76
fa03 0.74
un06 0.72
pr09 0.71
de01 0.71
fa10 0.69
pr02 0.69
ut07 0.68
co06 0.67
co04 0.66
pr06 0.65
de03 0.65
un03 0.64
co10 0.64
ut11 0.64
pr05 0.64
co03 0.63
de07 0.63
ut05 0.62
fa04 0.61
ut08 0.60
co08 0.60
co02 0.60
ut09 0.59
de05 0.59
de08 0.59
de06 0.58
de02 0.55
ut12 0.54
ut03 0.54
pr08 0.52
de10 0.52
pr07 0.52
fa08 0.51
co09 0.51
un09 0.51
co01 0.50
fa02 0.49
fa06 0.48
fa01 0.48
co05 0.48
un04 0.46
fa09 0.46
ut06 0.46
fa05 0.46
un07 0.46
pr01 0.45
un10 0.43
un12 0.43
un08 0.42
un01 0.41
ut01 0.40
un11 0.40
ut02 0.35
un05 0.34
un02 0.31

Confirmatory Factor Analysis

Basic model

Model:

mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"

CFA model fitting:

model1 <- cfa(mdl1, taia %>% select(all_of(taia_items_1)))
summary(model1)
## lavaan 0.6-8 ended normally after 62 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       139
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              6326.223
##   Degrees of freedom                              1814
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.727    0.055   13.199    0.000
##     pr03              0.386    0.060    6.384    0.000
##     pr04              0.153    0.063    2.431    0.015
##     pr05              0.870    0.068   12.721    0.000
##     pr06              0.808    0.061   13.285    0.000
##     pr07              1.040    0.061   17.095    0.000
##     pr08              0.847    0.050   17.029    0.000
##     pr09              0.714    0.054   13.167    0.000
##     pr10              0.666    0.060   11.105    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.896    0.061   14.596    0.000
##     co03              0.646    0.061   10.618    0.000
##     co04              0.459    0.065    7.065    0.000
##     co05              1.090    0.065   16.652    0.000
##     co06              0.856    0.066   13.052    0.000
##     co08              0.438    0.062    7.047    0.000
##     co09              0.978    0.063   15.527    0.000
##     co10              0.831    0.065   12.804    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.083    0.055   19.815    0.000
##     ut03              0.682    0.062   11.048    0.000
##     ut04              0.636    0.062   10.244    0.000
##     ut05              0.965    0.066   14.724    0.000
##     ut06              1.008    0.058   17.348    0.000
##     ut07              0.790    0.062   12.676    0.000
##     ut08              0.807    0.057   14.082    0.000
##     ut09              0.945    0.063   14.930    0.000
##     ut11              0.789    0.068   11.527    0.000
##     ut12              0.974    0.062   15.791    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.535    0.060    8.869    0.000
##     fa03              0.219    0.060    3.670    0.000
##     fa04              0.424    0.056    7.575    0.000
##     fa05              1.028    0.051   20.280    0.000
##     fa06              0.716    0.053   13.558    0.000
##     fa07              0.335    0.057    5.911    0.000
##     fa08              0.487    0.058    8.366    0.000
##     fa09              0.626    0.059   10.519    0.000
##     fa10              0.785    0.059   13.357    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.315    0.113   11.688    0.000
##     de03              1.183    0.111   10.668    0.000
##     de05              0.981    0.103    9.509    0.000
##     de06              1.276    0.116   11.019    0.000
##     de07              0.979    0.093   10.586    0.000
##     de08              1.185    0.102   11.579    0.000
##     de09              0.604    0.098    6.189    0.000
##     de10              1.377    0.116   11.853    0.000
##     de11              0.476    0.096    4.958    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.215    0.064   18.860    0.000
##     un03              0.817    0.068   11.959    0.000
##     un04              1.025    0.062   16.485    0.000
##     un05              1.142    0.063   18.233    0.000
##     un06              0.783    0.072   10.830    0.000
##     un07              1.040    0.068   15.336    0.000
##     un08              1.120    0.066   16.897    0.000
##     un09              1.090    0.071   15.404    0.000
##     un10              1.050    0.066   15.883    0.000
##     un11              1.192    0.069   17.382    0.000
##     un12              1.065    0.064   16.524    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR ~~                                               
##     CO                0.417    0.043    9.702    0.000
##     UT                0.479    0.045   10.634    0.000
##     FA                0.402    0.044    9.066    0.000
##     DE                0.423    0.045    9.495    0.000
##     UN                0.232    0.034    6.779    0.000
##   CO ~~                                               
##     UT                0.280    0.038    7.332    0.000
##     FA                0.355    0.044    8.018    0.000
##     DE                0.326    0.039    8.350    0.000
##     UN                0.171    0.033    5.125    0.000
##   UT ~~                                               
##     FA                0.331    0.043    7.706    0.000
##     DE                0.341    0.040    8.621    0.000
##     UN                0.187    0.034    5.560    0.000
##   FA ~~                                               
##     DE                0.366    0.043    8.534    0.000
##     UN                0.061    0.035    1.738    0.082
##   DE ~~                                               
##     UN                0.186    0.029    6.327    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.366    0.028   13.060    0.000
##    .pr02              0.619    0.041   14.916    0.000
##    .pr03              0.936    0.060   15.582    0.000
##    .pr04              1.071    0.068   15.711    0.000
##    .pr05              0.977    0.065   14.993    0.000
##    .pr06              0.751    0.050   14.902    0.000
##    .pr07              0.581    0.042   13.928    0.000
##    .pr08              0.390    0.028   13.953    0.000
##    .pr09              0.601    0.040   14.921    0.000
##    .pr10              0.805    0.053   15.208    0.000
##    .co01              0.526    0.040   13.135    0.000
##    .co02              0.573    0.041   13.824    0.000
##    .co03              0.780    0.052   15.010    0.000
##    .co04              1.045    0.068   15.460    0.000
##    .co05              0.481    0.039   12.355    0.000
##    .co06              0.763    0.053   14.429    0.000
##    .co08              0.957    0.062   15.462    0.000
##    .co09              0.536    0.040   13.296    0.000
##    .co10              0.763    0.053   14.505    0.000
##    .ut01              0.443    0.033   13.514    0.000
##    .ut02              0.333    0.027   12.266    0.000
##    .ut03              0.918    0.060   15.239    0.000
##    .ut04              0.959    0.063   15.322    0.000
##    .ut05              0.844    0.058   14.654    0.000
##    .ut06              0.528    0.038   13.844    0.000
##    .ut07              0.866    0.058   15.029    0.000
##    .ut08              0.674    0.046   14.788    0.000
##    .ut09              0.776    0.053   14.606    0.000
##    .ut11              1.107    0.073   15.183    0.000
##    .ut12              0.689    0.048   14.384    0.000
##    .fa01              0.393    0.036   11.016    0.000
##    .fa02              1.156    0.076   15.296    0.000
##    .fa03              1.245    0.079   15.664    0.000
##    .fa04              1.028    0.067   15.424    0.000
##    .fa05              0.348    0.034   10.153    0.000
##    .fa06              0.744    0.051   14.506    0.000
##    .fa07              1.092    0.070   15.551    0.000
##    .fa08              1.094    0.071   15.349    0.000
##    .fa09              1.071    0.071   15.086    0.000
##    .fa10              0.932    0.064   14.555    0.000
##    .de01              0.822    0.055   14.942    0.000
##    .de02              0.678    0.048   14.054    0.000
##    .de03              0.895    0.061   14.713    0.000
##    .de05              0.985    0.065   15.098    0.000
##    .de06              0.891    0.061   14.538    0.000
##    .de07              0.636    0.043   14.749    0.000
##    .de08              0.583    0.041   14.151    0.000
##    .de09              1.297    0.083   15.551    0.000
##    .de10              0.678    0.049   13.889    0.000
##    .de11              1.361    0.087   15.625    0.000
##    .un01              0.500    0.035   14.388    0.000
##    .un02              0.399    0.030   13.239    0.000
##    .un03              0.962    0.063   15.267    0.000
##    .un04              0.541    0.037   14.425    0.000
##    .un05              0.425    0.031   13.665    0.000
##    .un06              1.147    0.075   15.374    0.000
##    .un07              0.729    0.049   14.737    0.000
##    .un08              0.583    0.041   14.285    0.000
##    .un09              0.789    0.054   14.721    0.000
##    .un10              0.655    0.045   14.601    0.000
##    .un11              0.583    0.041   14.094    0.000
##    .un12              0.578    0.040   14.413    0.000
##     PR                0.605    0.059   10.207    0.000
##     CO                0.633    0.069    9.144    0.000
##     UT                0.659    0.066    9.942    0.000
##     FA                0.816    0.077   10.599    0.000
##     DE                0.377    0.058    6.490    0.000
##     UN                0.604    0.064    9.418    0.000

Fit measures:

tibble(
  `Model 1` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model1,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% 
    kable()
Model 1 Value
Chi-Squared 6326.2235
DF 1814.0000
p 0.0000
GFI 0.6381
AGFI 0.6104
CFI 0.7096
TLI 0.6973
SRMR 0.1010
RMSEA 0.0709

Standardized solution:

smodel1 <- standardizedsolution(model1)

Loadings:

smodel1 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Item Loading SE z p CI lower bound CI upper bound
PR =~ pr01 0.789 0.020 39.913 0.000 0.751 0.828
PR =~ pr02 0.584 0.032 18.261 0.000 0.521 0.647
PR =~ pr03 0.296 0.043 6.854 0.000 0.211 0.381
PR =~ pr04 0.114 0.047 2.456 0.014 0.023 0.206
PR =~ pr05 0.565 0.033 17.164 0.000 0.501 0.630
PR =~ pr06 0.587 0.032 18.467 0.000 0.525 0.650
PR =~ pr07 0.728 0.024 30.723 0.000 0.682 0.775
PR =~ pr08 0.726 0.024 30.439 0.000 0.679 0.773
PR =~ pr09 0.583 0.032 18.186 0.000 0.520 0.645
PR =~ pr10 0.500 0.036 13.888 0.000 0.429 0.570
CO =~ co01 0.739 0.024 30.666 0.000 0.692 0.786
CO =~ co02 0.686 0.027 25.127 0.000 0.632 0.739
CO =~ co03 0.503 0.037 13.698 0.000 0.431 0.575
CO =~ co04 0.337 0.043 7.853 0.000 0.253 0.421
CO =~ co05 0.781 0.022 36.274 0.000 0.739 0.823
CO =~ co06 0.615 0.031 19.688 0.000 0.554 0.676
CO =~ co08 0.336 0.043 7.829 0.000 0.252 0.420
CO =~ co09 0.728 0.025 29.429 0.000 0.680 0.777
CO =~ co10 0.604 0.032 18.958 0.000 0.541 0.666
UT =~ ut01 0.773 0.021 37.597 0.000 0.733 0.814
UT =~ ut02 0.836 0.016 51.142 0.000 0.804 0.868
UT =~ ut03 0.500 0.036 13.954 0.000 0.430 0.570
UT =~ ut04 0.466 0.037 12.493 0.000 0.393 0.539
UT =~ ut05 0.649 0.028 22.945 0.000 0.593 0.704
UT =~ ut06 0.748 0.022 33.612 0.000 0.704 0.791
UT =~ ut07 0.568 0.033 17.378 0.000 0.504 0.632
UT =~ ut08 0.624 0.030 21.010 0.000 0.566 0.682
UT =~ ut09 0.657 0.028 23.611 0.000 0.602 0.711
UT =~ ut11 0.520 0.035 14.890 0.000 0.452 0.589
UT =~ ut12 0.690 0.026 26.662 0.000 0.639 0.741
FA =~ fa01 0.822 0.019 42.299 0.000 0.784 0.860
FA =~ fa02 0.410 0.040 10.123 0.000 0.330 0.489
FA =~ fa03 0.175 0.047 3.752 0.000 0.083 0.266
FA =~ fa04 0.353 0.042 8.337 0.000 0.270 0.436
FA =~ fa05 0.844 0.018 46.444 0.000 0.809 0.880
FA =~ fa06 0.600 0.032 18.680 0.000 0.537 0.663
FA =~ fa07 0.279 0.044 6.264 0.000 0.191 0.366
FA =~ fa08 0.388 0.041 9.407 0.000 0.307 0.469
FA =~ fa09 0.479 0.038 12.693 0.000 0.405 0.553
FA =~ fa10 0.592 0.033 18.218 0.000 0.528 0.656
DE =~ de01 0.561 0.033 16.808 0.000 0.495 0.626
DE =~ de02 0.700 0.026 27.265 0.000 0.650 0.751
DE =~ de03 0.609 0.031 19.717 0.000 0.548 0.670
DE =~ de05 0.519 0.035 14.677 0.000 0.450 0.588
DE =~ de06 0.639 0.029 21.837 0.000 0.582 0.696
DE =~ de07 0.602 0.031 19.273 0.000 0.541 0.663
DE =~ de08 0.690 0.026 26.215 0.000 0.638 0.741
DE =~ de09 0.310 0.043 7.191 0.000 0.225 0.394
DE =~ de10 0.716 0.025 29.002 0.000 0.668 0.765
DE =~ de11 0.243 0.045 5.436 0.000 0.155 0.331
UN =~ un01 0.740 0.022 33.462 0.000 0.696 0.783
UN =~ un02 0.831 0.016 52.491 0.000 0.800 0.862
UN =~ un03 0.544 0.033 16.332 0.000 0.478 0.609
UN =~ un04 0.735 0.022 32.798 0.000 0.691 0.779
UN =~ un05 0.806 0.018 45.760 0.000 0.771 0.840
UN =~ un06 0.494 0.036 13.895 0.000 0.425 0.564
UN =~ un07 0.687 0.025 27.059 0.000 0.638 0.737
UN =~ un08 0.752 0.021 35.289 0.000 0.710 0.794
UN =~ un09 0.690 0.025 27.358 0.000 0.641 0.740
UN =~ un10 0.710 0.024 29.592 0.000 0.663 0.757
UN =~ un11 0.772 0.020 38.620 0.000 0.732 0.811
UN =~ un12 0.737 0.022 33.023 0.000 0.693 0.780

Covariances:

smodel1 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Factor Covariance SE z p CI lower bound CI upper bound
PR ~~ CO 0.674 0.032 21.123 0.000 0.611 0.736
PR ~~ UT 0.758 0.025 29.745 0.000 0.708 0.808
PR ~~ FA 0.572 0.037 15.251 0.000 0.498 0.645
PR ~~ DE 0.886 0.019 47.788 0.000 0.849 0.922
PR ~~ UN 0.383 0.043 8.811 0.000 0.298 0.469
CO ~~ UT 0.434 0.042 10.255 0.000 0.351 0.517
CO ~~ FA 0.493 0.041 12.022 0.000 0.413 0.574
CO ~~ DE 0.667 0.033 20.416 0.000 0.603 0.731
CO ~~ UN 0.276 0.047 5.929 0.000 0.185 0.367
UT ~~ FA 0.451 0.042 10.813 0.000 0.369 0.532
UT ~~ DE 0.684 0.030 22.435 0.000 0.624 0.744
UT ~~ UN 0.296 0.045 6.570 0.000 0.207 0.384
FA ~~ DE 0.659 0.033 19.845 0.000 0.594 0.724
FA ~~ UN 0.087 0.050 1.762 0.078 -0.010 0.185
DE ~~ UN 0.389 0.044 8.920 0.000 0.304 0.475

Residuals:

smodel1 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Item Residual SE z p CI lower bound CI upper bound
pr01 0.377 0.031 12.076 0 0.316 0.438
pr02 0.659 0.037 17.653 0 0.586 0.732
pr03 0.912 0.026 35.640 0 0.862 0.962
pr04 0.987 0.011 92.749 0 0.966 1.008
pr05 0.681 0.037 18.291 0 0.608 0.754
pr06 0.655 0.037 17.542 0 0.582 0.728
pr07 0.470 0.035 13.609 0 0.402 0.537
pr08 0.473 0.035 13.667 0 0.405 0.541
pr09 0.661 0.037 17.694 0 0.587 0.734
pr10 0.750 0.036 20.839 0 0.680 0.821
co01 0.454 0.036 12.730 0 0.384 0.523
co02 0.530 0.037 14.153 0 0.456 0.603
co03 0.747 0.037 20.238 0 0.675 0.820
co04 0.887 0.029 30.728 0 0.830 0.943
co05 0.390 0.034 11.593 0 0.324 0.456
co06 0.622 0.038 16.181 0 0.546 0.697
co08 0.887 0.029 30.806 0 0.831 0.944
co09 0.469 0.036 13.014 0 0.399 0.540
co10 0.636 0.038 16.536 0 0.560 0.711
ut01 0.402 0.032 12.630 0 0.340 0.464
ut02 0.301 0.027 11.006 0 0.247 0.354
ut03 0.750 0.036 20.907 0 0.679 0.820
ut04 0.783 0.035 22.500 0 0.715 0.851
ut05 0.579 0.037 15.774 0 0.507 0.651
ut06 0.441 0.033 13.251 0 0.376 0.506
ut07 0.678 0.037 18.290 0 0.605 0.751
ut08 0.611 0.037 16.492 0 0.538 0.683
ut09 0.568 0.037 15.554 0 0.497 0.640
ut11 0.729 0.036 20.063 0 0.658 0.801
ut12 0.524 0.036 14.683 0 0.454 0.594
fa01 0.325 0.032 10.177 0 0.262 0.387
fa02 0.832 0.033 25.070 0 0.767 0.897
fa03 0.969 0.016 59.581 0 0.938 1.001
fa04 0.875 0.030 29.210 0 0.816 0.934
fa05 0.287 0.031 9.357 0 0.227 0.347
fa06 0.640 0.039 16.624 0 0.565 0.716
fa07 0.922 0.025 37.218 0 0.874 0.971
fa08 0.849 0.032 26.527 0 0.787 0.912
fa09 0.770 0.036 21.268 0 0.699 0.841
fa10 0.649 0.038 16.872 0 0.574 0.725
de01 0.686 0.037 18.318 0 0.612 0.759
de02 0.510 0.036 14.162 0 0.439 0.580
de03 0.629 0.038 16.722 0 0.555 0.703
de05 0.731 0.037 19.910 0 0.659 0.803
de06 0.592 0.037 15.832 0 0.519 0.665
de07 0.637 0.038 16.934 0 0.564 0.711
de08 0.524 0.036 14.436 0 0.453 0.595
de09 0.904 0.027 33.919 0 0.852 0.956
de10 0.487 0.035 13.746 0 0.417 0.556
de11 0.941 0.022 43.330 0 0.898 0.984
un01 0.453 0.033 13.854 0 0.389 0.517
un02 0.309 0.026 11.747 0 0.258 0.361
un03 0.705 0.036 19.471 0 0.634 0.775
un04 0.460 0.033 13.962 0 0.395 0.524
un05 0.350 0.028 12.343 0 0.295 0.406
un06 0.756 0.035 21.495 0 0.687 0.825
un07 0.528 0.035 15.106 0 0.459 0.596
un08 0.435 0.032 13.574 0 0.372 0.498
un09 0.524 0.035 15.035 0 0.455 0.592
un10 0.496 0.034 14.549 0 0.429 0.563
un11 0.405 0.031 13.125 0 0.344 0.465
un12 0.458 0.033 13.925 0 0.393 0.522
PR 1.000 0.000 NA NA 1.000 1.000
CO 1.000 0.000 NA NA 1.000 1.000
UT 1.000 0.000 NA NA 1.000 1.000
FA 1.000 0.000 NA NA 1.000 1.000
DE 1.000 0.000 NA NA 1.000 1.000
UN 1.000 0.000 NA NA 1.000 1.000

Visualization:

semPaths(model1,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Model with general factor

Model:

mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"
model2 <- cfa(mdl2, taia %>% select(all_of(taia_items_1)))
summary(model2)
## lavaan 0.6-8 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       130
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              6381.193
##   Degrees of freedom                              1823
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.732    0.055   13.227    0.000
##     pr03              0.389    0.061    6.409    0.000
##     pr04              0.160    0.063    2.533    0.011
##     pr05              0.882    0.069   12.845    0.000
##     pr06              0.797    0.061   13.006    0.000
##     pr07              1.049    0.061   17.137    0.000
##     pr08              0.846    0.050   16.877    0.000
##     pr09              0.719    0.055   13.186    0.000
##     pr10              0.661    0.060   10.962    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.894    0.062   14.491    0.000
##     co03              0.652    0.061   10.699    0.000
##     co04              0.473    0.065    7.263    0.000
##     co05              1.088    0.066   16.548    0.000
##     co06              0.862    0.066   13.089    0.000
##     co08              0.435    0.062    6.976    0.000
##     co09              0.977    0.063   15.442    0.000
##     co10              0.834    0.065   12.798    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.080    0.055   19.757    0.000
##     ut03              0.674    0.062   10.910    0.000
##     ut04              0.635    0.062   10.237    0.000
##     ut05              0.972    0.065   14.837    0.000
##     ut06              1.007    0.058   17.336    0.000
##     ut07              0.791    0.062   12.699    0.000
##     ut08              0.807    0.057   14.089    0.000
##     ut09              0.946    0.063   14.941    0.000
##     ut11              0.790    0.068   11.534    0.000
##     ut12              0.973    0.062   15.774    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.522    0.060    8.760    0.000
##     fa03              0.204    0.059    3.452    0.001
##     fa04              0.407    0.055    7.352    0.000
##     fa05              1.017    0.050   20.447    0.000
##     fa06              0.704    0.052   13.544    0.000
##     fa07              0.323    0.056    5.749    0.000
##     fa08              0.475    0.058    8.239    0.000
##     fa09              0.616    0.059   10.488    0.000
##     fa10              0.780    0.058   13.496    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.314    0.113   11.628    0.000
##     de03              1.176    0.111   10.576    0.000
##     de05              1.006    0.104    9.645    0.000
##     de06              1.267    0.116   10.914    0.000
##     de07              0.989    0.093   10.609    0.000
##     de08              1.188    0.103   11.539    0.000
##     de09              0.623    0.098    6.342    0.000
##     de10              1.371    0.117   11.764    0.000
##     de11              0.475    0.096    4.934    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.935    0.000
##     un03              0.811    0.068   11.917    0.000
##     un04              1.022    0.062   16.530    0.000
##     un05              1.140    0.062   18.326    0.000
##     un06              0.780    0.072   10.831    0.000
##     un07              1.035    0.067   15.346    0.000
##     un08              1.118    0.066   16.974    0.000
##     un09              1.085    0.070   15.412    0.000
##     un10              1.046    0.066   15.902    0.000
##     un11              1.188    0.068   17.430    0.000
##     un12              1.061    0.064   16.562    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.746    0.061   12.327    0.000
##     UT                0.818    0.060   13.693    0.000
##     FA                0.781    0.064   12.133    0.000
##     DE                0.777    0.066   11.697    0.000
##     UN                0.408    0.053    7.651    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.369    0.028   13.031    0.000
##    .pr02              0.616    0.041   14.883    0.000
##    .pr03              0.935    0.060   15.576    0.000
##    .pr04              1.070    0.068   15.709    0.000
##    .pr05              0.966    0.065   14.947    0.000
##    .pr06              0.763    0.051   14.921    0.000
##    .pr07              0.573    0.041   13.836    0.000
##    .pr08              0.393    0.028   13.938    0.000
##    .pr09              0.599    0.040   14.890    0.000
##    .pr10              0.810    0.053   15.208    0.000
##    .co01              0.528    0.040   13.119    0.000
##    .co02              0.578    0.042   13.832    0.000
##    .co03              0.775    0.052   14.982    0.000
##    .co04              1.037    0.067   15.438    0.000
##    .co05              0.485    0.039   12.362    0.000
##    .co06              0.759    0.053   14.391    0.000
##    .co08              0.959    0.062   15.463    0.000
##    .co09              0.539    0.041   13.290    0.000
##    .co10              0.761    0.053   14.482    0.000
##    .ut01              0.442    0.033   13.486    0.000
##    .ut02              0.336    0.027   12.276    0.000
##    .ut03              0.925    0.061   15.248    0.000
##    .ut04              0.959    0.063   15.318    0.000
##    .ut05              0.836    0.057   14.615    0.000
##    .ut06              0.528    0.038   13.826    0.000
##    .ut07              0.864    0.058   15.017    0.000
##    .ut08              0.673    0.046   14.776    0.000
##    .ut09              0.775    0.053   14.590    0.000
##    .ut11              1.106    0.073   15.176    0.000
##    .ut12              0.690    0.048   14.373    0.000
##    .fa01              0.375    0.035   10.619    0.000
##    .fa02              1.162    0.076   15.307    0.000
##    .fa03              1.250    0.080   15.672    0.000
##    .fa04              1.036    0.067   15.443    0.000
##    .fa05              0.348    0.035   10.076    0.000
##    .fa06              0.748    0.052   14.517    0.000
##    .fa07              1.097    0.070   15.560    0.000
##    .fa08              1.100    0.072   15.361    0.000
##    .fa09              1.074    0.071   15.090    0.000
##    .fa10              0.927    0.064   14.529    0.000
##    .de01              0.823    0.055   14.925    0.000
##    .de02              0.681    0.049   14.028    0.000
##    .de03              0.904    0.061   14.713    0.000
##    .de05              0.968    0.064   15.039    0.000
##    .de06              0.902    0.062   14.545    0.000
##    .de07              0.630    0.043   14.698    0.000
##    .de08              0.583    0.041   14.109    0.000
##    .de09              1.288    0.083   15.533    0.000
##    .de10              0.687    0.049   13.891    0.000
##    .de11              1.361    0.087   15.623    0.000
##    .un01              0.497    0.035   14.367    0.000
##    .un02              0.399    0.030   13.228    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.422    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.148    0.075   15.374    0.000
##    .un07              0.732    0.050   14.740    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.725    0.000
##    .un10              0.657    0.045   14.604    0.000
##    .un11              0.584    0.041   14.091    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.051    0.017    2.954    0.003
##    .CO                0.324    0.039    8.231    0.000
##    .UT                0.290    0.033    8.734    0.000
##    .FA                0.498    0.051    9.744    0.000
##    .DE                0.043    0.012    3.438    0.001
##    .UN                0.516    0.055    9.336    0.000
##     DT                0.552    0.058    9.531    0.000
tibble(
  `Model 2` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model2,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()
Model 2 Value
Chi-Squared 6381.1932
DF 1823.0000
p 0.0000
GFI 0.6329
AGFI 0.6068
CFI 0.7067
TLI 0.6957
SRMR 0.1029
RMSEA 0.0711

Standardized solution:

smodel2 <- standardizedsolution(model2)

Loadings:

smodel2 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Item Loading SE z p CI lower bound CI upper bound
PR =~ pr01 0.787 0.020 39.421 0.00 0.748 0.827
PR =~ pr02 0.587 0.032 18.392 0.00 0.524 0.649
PR =~ pr03 0.298 0.043 6.889 0.00 0.213 0.383
PR =~ pr04 0.119 0.047 2.561 0.01 0.028 0.210
PR =~ pr05 0.572 0.033 17.497 0.00 0.508 0.636
PR =~ pr06 0.578 0.032 17.869 0.00 0.515 0.641
PR =~ pr07 0.732 0.024 31.152 0.00 0.686 0.778
PR =~ pr08 0.723 0.024 30.023 0.00 0.676 0.770
PR =~ pr09 0.585 0.032 18.294 0.00 0.522 0.648
PR =~ pr10 0.495 0.036 13.654 0.00 0.424 0.566
CO =~ co01 0.738 0.024 30.465 0.00 0.691 0.786
CO =~ co02 0.683 0.028 24.809 0.00 0.629 0.737
CO =~ co03 0.507 0.037 13.884 0.00 0.436 0.579
CO =~ co04 0.346 0.043 8.129 0.00 0.263 0.430
CO =~ co05 0.779 0.022 35.829 0.00 0.736 0.821
CO =~ co06 0.618 0.031 19.860 0.00 0.557 0.679
CO =~ co08 0.333 0.043 7.737 0.00 0.248 0.417
CO =~ co09 0.727 0.025 29.164 0.00 0.678 0.776
CO =~ co10 0.605 0.032 18.998 0.00 0.542 0.667
UT =~ ut01 0.774 0.021 37.585 0.00 0.733 0.814
UT =~ ut02 0.834 0.017 50.561 0.00 0.802 0.867
UT =~ ut03 0.495 0.036 13.693 0.00 0.424 0.565
UT =~ ut04 0.466 0.037 12.481 0.00 0.393 0.539
UT =~ ut05 0.654 0.028 23.305 0.00 0.599 0.708
UT =~ ut06 0.748 0.022 33.540 0.00 0.704 0.791
UT =~ ut07 0.569 0.033 17.431 0.00 0.505 0.633
UT =~ ut08 0.624 0.030 21.028 0.00 0.566 0.682
UT =~ ut09 0.658 0.028 23.643 0.00 0.603 0.712
UT =~ ut11 0.521 0.035 14.903 0.00 0.452 0.589
UT =~ ut12 0.689 0.026 26.593 0.00 0.639 0.740
FA =~ fa01 0.831 0.019 43.634 0.00 0.793 0.868
FA =~ fa02 0.404 0.041 9.937 0.00 0.325 0.484
FA =~ fa03 0.164 0.047 3.519 0.00 0.073 0.256
FA =~ fa04 0.343 0.043 8.031 0.00 0.259 0.427
FA =~ fa05 0.844 0.018 46.107 0.00 0.808 0.880
FA =~ fa06 0.597 0.032 18.480 0.00 0.533 0.660
FA =~ fa07 0.271 0.045 6.067 0.00 0.183 0.359
FA =~ fa08 0.382 0.041 9.209 0.00 0.301 0.463
FA =~ fa09 0.477 0.038 12.585 0.00 0.403 0.551
FA =~ fa10 0.595 0.032 18.373 0.00 0.531 0.658
DE =~ de01 0.560 0.033 16.714 0.00 0.494 0.625
DE =~ de02 0.699 0.026 26.998 0.00 0.648 0.749
DE =~ de03 0.604 0.031 19.352 0.00 0.543 0.665
DE =~ de05 0.531 0.035 15.227 0.00 0.463 0.599
DE =~ de06 0.633 0.030 21.338 0.00 0.575 0.691
DE =~ de07 0.607 0.031 19.535 0.00 0.546 0.668
DE =~ de08 0.690 0.026 26.154 0.00 0.638 0.742
DE =~ de09 0.319 0.043 7.441 0.00 0.235 0.403
DE =~ de10 0.712 0.025 28.410 0.00 0.663 0.761
DE =~ de11 0.242 0.045 5.408 0.00 0.154 0.330
UN =~ un01 0.742 0.022 33.751 0.00 0.699 0.785
UN =~ un02 0.831 0.016 52.514 0.00 0.800 0.862
UN =~ un03 0.541 0.033 16.192 0.00 0.476 0.606
UN =~ un04 0.735 0.022 32.778 0.00 0.691 0.779
UN =~ un05 0.807 0.018 46.027 0.00 0.773 0.842
UN =~ un06 0.494 0.036 13.869 0.00 0.424 0.563
UN =~ un07 0.686 0.025 26.938 0.00 0.636 0.736
UN =~ un08 0.753 0.021 35.450 0.00 0.711 0.795
UN =~ un09 0.689 0.025 27.222 0.00 0.639 0.739
UN =~ un10 0.709 0.024 29.476 0.00 0.662 0.756
UN =~ un11 0.771 0.020 38.554 0.00 0.732 0.811
UN =~ un12 0.736 0.022 32.957 0.00 0.692 0.780
DT =~ PR 0.957 0.015 65.735 0.00 0.928 0.985
DT =~ CO 0.697 0.030 23.591 0.00 0.639 0.755
DT =~ UT 0.748 0.025 29.635 0.00 0.699 0.798
DT =~ FA 0.635 0.033 19.234 0.00 0.571 0.700
DT =~ DE 0.941 0.016 60.642 0.00 0.911 0.972
DT =~ UN 0.389 0.042 9.209 0.00 0.306 0.472

Covariances:

smodel2 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Factor Covariance SE z p CI lower bound CI upper bound

Residuals:

smodel2 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Item Residual SE z p CI lower bound CI upper bound
pr01 0.380 0.031 12.074 0.000 0.318 0.442
pr02 0.656 0.037 17.528 0.000 0.583 0.729
pr03 0.911 0.026 35.398 0.000 0.861 0.962
pr04 0.986 0.011 88.834 0.000 0.964 1.008
pr05 0.673 0.037 18.033 0.000 0.600 0.747
pr06 0.666 0.037 17.817 0.000 0.593 0.739
pr07 0.464 0.034 13.462 0.000 0.396 0.531
pr08 0.477 0.035 13.693 0.000 0.409 0.545
pr09 0.658 0.037 17.581 0.000 0.584 0.731
pr10 0.755 0.036 21.013 0.000 0.684 0.825
co01 0.455 0.036 12.729 0.000 0.385 0.525
co02 0.534 0.038 14.210 0.000 0.460 0.608
co03 0.743 0.037 20.019 0.000 0.670 0.815
co04 0.880 0.030 29.818 0.000 0.822 0.938
co05 0.393 0.034 11.621 0.000 0.327 0.460
co06 0.618 0.038 16.064 0.000 0.543 0.693
co08 0.889 0.029 31.060 0.000 0.833 0.945
co09 0.472 0.036 13.032 0.000 0.401 0.543
co10 0.634 0.038 16.479 0.000 0.559 0.710
ut01 0.401 0.032 12.604 0.000 0.339 0.464
ut02 0.304 0.028 11.029 0.000 0.250 0.358
ut03 0.755 0.036 21.142 0.000 0.685 0.825
ut04 0.783 0.035 22.492 0.000 0.715 0.851
ut05 0.573 0.037 15.632 0.000 0.501 0.645
ut06 0.441 0.033 13.238 0.000 0.376 0.506
ut07 0.677 0.037 18.236 0.000 0.604 0.749
ut08 0.610 0.037 16.463 0.000 0.538 0.683
ut09 0.568 0.037 15.522 0.000 0.496 0.639
ut11 0.729 0.036 20.030 0.000 0.658 0.800
ut12 0.525 0.036 14.678 0.000 0.455 0.595
fa01 0.310 0.032 9.805 0.000 0.248 0.372
fa02 0.836 0.033 25.404 0.000 0.772 0.901
fa03 0.973 0.015 63.285 0.000 0.943 1.003
fa04 0.882 0.029 30.092 0.000 0.825 0.940
fa05 0.287 0.031 9.297 0.000 0.227 0.348
fa06 0.644 0.039 16.709 0.000 0.568 0.719
fa07 0.927 0.024 38.250 0.000 0.879 0.974
fa08 0.854 0.032 26.951 0.000 0.792 0.916
fa09 0.773 0.036 21.374 0.000 0.702 0.843
fa10 0.646 0.039 16.767 0.000 0.571 0.722
de01 0.687 0.037 18.317 0.000 0.613 0.760
de02 0.512 0.036 14.164 0.000 0.441 0.583
de03 0.635 0.038 16.835 0.000 0.561 0.709
de05 0.718 0.037 19.391 0.000 0.645 0.791
de06 0.599 0.038 15.964 0.000 0.526 0.673
de07 0.632 0.038 16.747 0.000 0.558 0.706
de08 0.524 0.036 14.387 0.000 0.452 0.595
de09 0.898 0.027 32.863 0.000 0.845 0.952
de10 0.493 0.036 13.816 0.000 0.423 0.563
de11 0.941 0.022 43.416 0.000 0.899 0.984
un01 0.450 0.033 13.802 0.000 0.386 0.514
un02 0.309 0.026 11.737 0.000 0.257 0.361
un03 0.707 0.036 19.565 0.000 0.636 0.778
un04 0.460 0.033 13.960 0.000 0.395 0.525
un05 0.349 0.028 12.311 0.000 0.293 0.404
un06 0.756 0.035 21.515 0.000 0.687 0.825
un07 0.529 0.035 15.131 0.000 0.461 0.598
un08 0.433 0.032 13.545 0.000 0.370 0.496
un09 0.525 0.035 15.062 0.000 0.457 0.594
un10 0.497 0.034 14.567 0.000 0.430 0.564
un11 0.405 0.031 13.127 0.000 0.345 0.466
un12 0.458 0.033 13.930 0.000 0.394 0.523
PR 0.084 0.028 3.025 0.002 0.030 0.139
CO 0.514 0.041 12.465 0.000 0.433 0.595
UT 0.440 0.038 11.638 0.000 0.366 0.514
FA 0.596 0.042 14.207 0.000 0.514 0.679
DE 0.114 0.029 3.897 0.000 0.057 0.171
UN 0.849 0.033 25.829 0.000 0.784 0.913
DT 1.000 0.000 NA NA 1.000 1.000
semPaths(model2,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Items Exclusion. Second step

Excluded items: pr03, pr04, fa03, fa07, de11

Reason: low factor loadings

Stems:

  • pr03 Я считаю, что интеллектуальные системы ненадежны (R)
  • pr04 Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
  • fa03 Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
  • fa07 Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
  • de11 Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)
pr_items_2 <- pr_items_0[-c(3, 4)]
co_items_2 <- co_items_0[-7]
ut_items_2 <- ut_items_0[-10]
fa_items_2 <- fa_items_0[-c(3, 7)]
de_items_2 <- de_items_0[-c(4, 11)]
un_items_2 <- un_items_0
taia_items_2 <- c(pr_items_2,
                  co_items_2,
                  ut_items_2,
                  fa_items_2,
                  de_items_2,
                  un_items_2)

CFA with new item set

Model:

mdl3 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"
model3 <- cfa(mdl3, taia %>% select(all_of(taia_items_2)))
summary(model3)
## lavaan 0.6-8 ended normally after 45 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       120
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              5261.509
##   Degrees of freedom                              1533
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.738    0.056   13.173    0.000
##     pr05              0.890    0.069   12.819    0.000
##     pr06              0.805    0.062   12.996    0.000
##     pr07              1.053    0.062   16.945    0.000
##     pr08              0.852    0.051   16.773    0.000
##     pr09              0.727    0.055   13.194    0.000
##     pr10              0.669    0.061   11.001    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.894    0.062   14.492    0.000
##     co03              0.653    0.061   10.711    0.000
##     co04              0.474    0.065    7.277    0.000
##     co05              1.088    0.066   16.546    0.000
##     co06              0.862    0.066   13.089    0.000
##     co08              0.435    0.062    6.978    0.000
##     co09              0.977    0.063   15.433    0.000
##     co10              0.835    0.065   12.814    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.080    0.055   19.757    0.000
##     ut03              0.671    0.062   10.868    0.000
##     ut04              0.635    0.062   10.231    0.000
##     ut05              0.972    0.065   14.846    0.000
##     ut06              1.007    0.058   17.348    0.000
##     ut07              0.792    0.062   12.718    0.000
##     ut08              0.807    0.057   14.098    0.000
##     ut09              0.945    0.063   14.939    0.000
##     ut11              0.789    0.068   11.523    0.000
##     ut12              0.973    0.062   15.779    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.516    0.059    8.728    0.000
##     fa04              0.397    0.055    7.215    0.000
##     fa05              1.010    0.049   20.518    0.000
##     fa06              0.691    0.052   13.376    0.000
##     fa08              0.468    0.057    8.178    0.000
##     fa09              0.604    0.058   10.363    0.000
##     fa10              0.785    0.057   13.732    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.310    0.112   11.675    0.000
##     de03              1.170    0.110   10.597    0.000
##     de05              0.998    0.104    9.640    0.000
##     de06              1.255    0.115   10.911    0.000
##     de07              0.988    0.093   10.664    0.000
##     de08              1.187    0.102   11.604    0.000
##     de09              0.603    0.097    6.189    0.000
##     de10              1.363    0.116   11.794    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.933    0.000
##     un03              0.811    0.068   11.917    0.000
##     un04              1.022    0.062   16.529    0.000
##     un05              1.140    0.062   18.327    0.000
##     un06              0.780    0.072   10.824    0.000
##     un07              1.035    0.067   15.351    0.000
##     un08              1.118    0.066   16.974    0.000
##     un09              1.085    0.070   15.413    0.000
##     un10              1.046    0.066   15.904    0.000
##     un11              1.188    0.068   17.430    0.000
##     un12              1.061    0.064   16.562    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.756    0.061   12.410    0.000
##     UT                0.817    0.060   13.611    0.000
##     FA                0.774    0.065   11.940    0.000
##     DE                0.781    0.067   11.715    0.000
##     UN                0.415    0.054    7.755    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.377    0.029   13.111    0.000
##    .pr02              0.616    0.041   14.878    0.000
##    .pr05              0.964    0.065   14.939    0.000
##    .pr06              0.761    0.051   14.909    0.000
##    .pr07              0.577    0.042   13.851    0.000
##    .pr08              0.392    0.028   13.920    0.000
##    .pr09              0.596    0.040   14.874    0.000
##    .pr10              0.807    0.053   15.197    0.000
##    .co01              0.528    0.040   13.134    0.000
##    .co02              0.578    0.042   13.840    0.000
##    .co03              0.774    0.052   14.983    0.000
##    .co04              1.037    0.067   15.438    0.000
##    .co05              0.485    0.039   12.379    0.000
##    .co06              0.759    0.053   14.396    0.000
##    .co08              0.959    0.062   15.464    0.000
##    .co09              0.540    0.041   13.307    0.000
##    .co10              0.760    0.052   14.482    0.000
##    .ut01              0.442    0.033   13.480    0.000
##    .ut02              0.336    0.027   12.276    0.000
##    .ut03              0.927    0.061   15.252    0.000
##    .ut04              0.959    0.063   15.318    0.000
##    .ut05              0.835    0.057   14.611    0.000
##    .ut06              0.528    0.038   13.820    0.000
##    .ut07              0.863    0.058   15.013    0.000
##    .ut08              0.672    0.046   14.773    0.000
##    .ut09              0.775    0.053   14.590    0.000
##    .ut11              1.106    0.073   15.177    0.000
##    .ut12              0.690    0.048   14.370    0.000
##    .fa01              0.364    0.035   10.321    0.000
##    .fa02              1.164    0.076   15.308    0.000
##    .fa04              1.041    0.067   15.452    0.000
##    .fa05              0.347    0.035    9.969    0.000
##    .fa06              0.759    0.052   14.556    0.000
##    .fa08              1.102    0.072   15.365    0.000
##    .fa09              1.081    0.072   15.106    0.000
##    .fa10              0.914    0.063   14.471    0.000
##    .de01              0.820    0.055   14.910    0.000
##    .de02              0.680    0.049   14.009    0.000
##    .de03              0.905    0.062   14.710    0.000
##    .de05              0.971    0.065   15.041    0.000
##    .de06              0.908    0.062   14.556    0.000
##    .de07              0.627    0.043   14.679    0.000
##    .de08              0.579    0.041   14.074    0.000
##    .de09              1.296    0.083   15.544    0.000
##    .de10              0.690    0.050   13.890    0.000
##    .un01              0.497    0.035   14.368    0.000
##    .un02              0.399    0.030   13.230    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.423    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.148    0.075   15.374    0.000
##    .un07              0.731    0.050   14.739    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.725    0.000
##    .un10              0.657    0.045   14.604    0.000
##    .un11              0.584    0.041   14.092    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.045    0.017    2.670    0.008
##    .CO                0.317    0.039    8.191    0.000
##    .UT                0.294    0.034    8.763    0.000
##    .FA                0.516    0.052    9.864    0.000
##    .DE                0.044    0.013    3.465    0.001
##    .UN                0.513    0.055    9.332    0.000
##     DT                0.549    0.058    9.485    0.000
tibble(
  `Model 3` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model3,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()
Model 3 Value
Chi-Squared 5261.5090
DF 1533.0000
p 0.0000
GFI 0.6731
AGFI 0.6476
CFI 0.7446
TLI 0.7341
SRMR 0.1013
RMSEA 0.0701

Standardized solution:

smodel3 <- standardizedsolution(model3)

Loadings:

smodel3 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Item Loading SE z p CI lower bound CI upper bound
PR =~ pr01 0.782 0.020 38.417 0 0.742 0.822
PR =~ pr02 0.587 0.032 18.400 0 0.524 0.649
PR =~ pr05 0.573 0.033 17.559 0 0.509 0.637
PR =~ pr06 0.580 0.032 17.974 0 0.517 0.643
PR =~ pr07 0.730 0.024 30.840 0 0.684 0.776
PR =~ pr08 0.724 0.024 30.081 0 0.677 0.771
PR =~ pr09 0.588 0.032 18.451 0 0.525 0.650
PR =~ pr10 0.498 0.036 13.787 0 0.427 0.569
CO =~ co01 0.738 0.024 30.452 0 0.690 0.785
CO =~ co02 0.683 0.028 24.821 0 0.629 0.737
CO =~ co03 0.508 0.037 13.911 0 0.436 0.580
CO =~ co04 0.347 0.043 8.149 0 0.264 0.430
CO =~ co05 0.779 0.022 35.840 0 0.736 0.821
CO =~ co06 0.618 0.031 19.865 0 0.557 0.679
CO =~ co08 0.333 0.043 7.740 0 0.249 0.417
CO =~ co09 0.726 0.025 29.132 0 0.677 0.775
CO =~ co10 0.605 0.032 19.048 0 0.543 0.668
UT =~ ut01 0.774 0.021 37.612 0 0.734 0.814
UT =~ ut02 0.834 0.017 50.496 0 0.802 0.867
UT =~ ut03 0.493 0.036 13.613 0 0.422 0.564
UT =~ ut04 0.466 0.037 12.469 0 0.393 0.539
UT =~ ut05 0.654 0.028 23.325 0 0.599 0.709
UT =~ ut06 0.748 0.022 33.579 0 0.704 0.792
UT =~ ut07 0.569 0.033 17.471 0 0.506 0.633
UT =~ ut08 0.625 0.030 21.049 0 0.566 0.683
UT =~ ut09 0.657 0.028 23.629 0 0.603 0.712
UT =~ ut11 0.520 0.035 14.879 0 0.452 0.589
UT =~ ut12 0.690 0.026 26.601 0 0.639 0.740
FA =~ fa01 0.836 0.019 44.351 0 0.799 0.873
FA =~ fa02 0.403 0.041 9.877 0 0.323 0.483
FA =~ fa04 0.337 0.043 7.848 0 0.253 0.421
FA =~ fa05 0.845 0.018 45.872 0 0.809 0.881
FA =~ fa06 0.589 0.033 18.019 0 0.525 0.653
FA =~ fa08 0.379 0.042 9.114 0 0.298 0.461
FA =~ fa09 0.471 0.038 12.352 0 0.396 0.546
FA =~ fa10 0.602 0.032 18.816 0 0.540 0.665
DE =~ de01 0.562 0.033 16.829 0 0.497 0.627
DE =~ de02 0.699 0.026 27.038 0 0.649 0.750
DE =~ de03 0.603 0.031 19.291 0 0.542 0.665
DE =~ de05 0.529 0.035 15.120 0 0.460 0.598
DE =~ de06 0.630 0.030 21.094 0 0.571 0.688
DE =~ de07 0.609 0.031 19.655 0 0.548 0.670
DE =~ de08 0.692 0.026 26.365 0 0.641 0.744
DE =~ de09 0.310 0.043 7.189 0 0.226 0.395
DE =~ de10 0.711 0.025 28.245 0 0.661 0.760
UN =~ un01 0.742 0.022 33.751 0 0.699 0.785
UN =~ un02 0.831 0.016 52.499 0 0.800 0.862
UN =~ un03 0.541 0.033 16.192 0 0.476 0.606
UN =~ un04 0.735 0.022 32.773 0 0.691 0.779
UN =~ un05 0.807 0.018 46.033 0 0.773 0.842
UN =~ un06 0.493 0.036 13.855 0 0.424 0.563
UN =~ un07 0.686 0.025 26.955 0 0.637 0.736
UN =~ un08 0.753 0.021 35.453 0 0.711 0.795
UN =~ un09 0.689 0.025 27.225 0 0.639 0.739
UN =~ un10 0.709 0.024 29.486 0 0.662 0.756
UN =~ un11 0.771 0.020 38.556 0 0.732 0.810
UN =~ un12 0.736 0.022 32.962 0 0.692 0.780
DT =~ PR 0.961 0.014 66.441 0 0.933 0.990
DT =~ CO 0.705 0.029 24.250 0 0.648 0.762
DT =~ UT 0.745 0.025 29.284 0 0.695 0.795
DT =~ FA 0.624 0.034 18.548 0 0.558 0.690
DT =~ DE 0.941 0.016 60.324 0 0.910 0.971
DT =~ UN 0.395 0.042 9.399 0 0.313 0.477

Covariances:

smodel3 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Factor Covariance SE z p CI lower bound CI upper bound

Residuals:

smodel3 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Item Residual SE z p CI lower bound CI upper bound
pr01 0.388 0.032 12.200 0.000 0.326 0.451
pr02 0.656 0.037 17.513 0.000 0.582 0.729
pr05 0.672 0.037 17.986 0.000 0.599 0.745
pr06 0.664 0.037 17.747 0.000 0.591 0.737
pr07 0.467 0.035 13.509 0.000 0.399 0.535
pr08 0.476 0.035 13.666 0.000 0.408 0.544
pr09 0.655 0.037 17.485 0.000 0.581 0.728
pr10 0.752 0.036 20.870 0.000 0.681 0.822
co01 0.456 0.036 12.746 0.000 0.386 0.526
co02 0.534 0.038 14.218 0.000 0.460 0.608
co03 0.742 0.037 20.004 0.000 0.669 0.815
co04 0.880 0.030 29.771 0.000 0.822 0.938
co05 0.394 0.034 11.637 0.000 0.327 0.460
co06 0.618 0.038 16.073 0.000 0.543 0.693
co08 0.889 0.029 31.064 0.000 0.833 0.945
co09 0.473 0.036 13.054 0.000 0.402 0.544
co10 0.634 0.038 16.465 0.000 0.558 0.709
ut01 0.401 0.032 12.597 0.000 0.339 0.464
ut02 0.304 0.028 11.030 0.000 0.250 0.358
ut03 0.757 0.036 21.220 0.000 0.687 0.827
ut04 0.783 0.035 22.504 0.000 0.715 0.851
ut05 0.573 0.037 15.622 0.000 0.501 0.644
ut06 0.441 0.033 13.228 0.000 0.375 0.506
ut07 0.676 0.037 18.209 0.000 0.603 0.749
ut08 0.610 0.037 16.452 0.000 0.537 0.683
ut09 0.568 0.037 15.523 0.000 0.496 0.640
ut11 0.729 0.036 20.046 0.000 0.658 0.801
ut12 0.525 0.036 14.674 0.000 0.454 0.595
fa01 0.301 0.032 9.535 0.000 0.239 0.363
fa02 0.838 0.033 25.501 0.000 0.773 0.902
fa04 0.886 0.029 30.644 0.000 0.830 0.943
fa05 0.287 0.031 9.210 0.000 0.226 0.347
fa06 0.653 0.039 16.940 0.000 0.577 0.728
fa08 0.856 0.032 27.148 0.000 0.794 0.918
fa09 0.778 0.036 21.634 0.000 0.707 0.848
fa10 0.637 0.039 16.511 0.000 0.561 0.713
de01 0.684 0.038 18.225 0.000 0.611 0.758
de02 0.511 0.036 14.134 0.000 0.440 0.582
de03 0.636 0.038 16.847 0.000 0.562 0.710
de05 0.720 0.037 19.459 0.000 0.648 0.793
de06 0.603 0.038 16.045 0.000 0.530 0.677
de07 0.629 0.038 16.673 0.000 0.555 0.703
de08 0.521 0.036 14.311 0.000 0.449 0.592
de09 0.904 0.027 33.806 0.000 0.851 0.956
de10 0.495 0.036 13.835 0.000 0.425 0.565
un01 0.450 0.033 13.803 0.000 0.386 0.514
un02 0.309 0.026 11.739 0.000 0.257 0.361
un03 0.707 0.036 19.566 0.000 0.636 0.778
un04 0.460 0.033 13.962 0.000 0.395 0.525
un05 0.348 0.028 12.311 0.000 0.293 0.404
un06 0.757 0.035 21.530 0.000 0.688 0.825
un07 0.529 0.035 15.127 0.000 0.460 0.597
un08 0.433 0.032 13.546 0.000 0.370 0.496
un09 0.525 0.035 15.062 0.000 0.457 0.594
un10 0.497 0.034 14.566 0.000 0.430 0.564
un11 0.405 0.031 13.128 0.000 0.345 0.466
un12 0.458 0.033 13.930 0.000 0.394 0.523
PR 0.076 0.028 2.729 0.006 0.021 0.130
CO 0.503 0.041 12.262 0.000 0.422 0.583
UT 0.445 0.038 11.731 0.000 0.370 0.519
FA 0.611 0.042 14.542 0.000 0.528 0.693
DE 0.115 0.029 3.932 0.000 0.058 0.173
UN 0.844 0.033 25.430 0.000 0.779 0.909
DT 1.000 0.000 NA NA 1.000 1.000
semPaths(model3,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Predicted values

lavPredict(model3, taia %>% select(all_of(taia_items_2))) %>% 
  as_tibble() %>% 
  mutate(id = taia$id) %>% 
  full_join(
    taia %>% 
      select(id, all_of(taia_items_2)) %>% 
      pivot_longer(cols = all_of(taia_items_2)) %>% 
      mutate(name = toupper(str_replace_all(name, "[:digit:]{2}", ""))) %>% 
      group_by(id, name) %>% 
      summarise(score = sum(value)) %>% 
      pivot_wider(id_cols = id, names_from = name, names_prefix = "s_", values_from = score) %>% 
      relocate(after = c(id, s_PR, s_CO, s_UT, s_FA, s_DE, s_UN)) %>% 
      mutate(s_DT = s_PR + s_CO + s_UT + s_FA + s_DE + s_UN)
  ) -> predicted_and_direct_sums
## `summarise()` regrouping output by 'id' (override with `.groups` argument)
## Joining, by = "id"
ggcorrplot(cor(predicted_and_direct_sums %>% select(-id))[-(1:7), -(8:14)],
           lab = TRUE,
           colors = c("indianred1", "white", "royalblue1"))

Graph shows high correlation between fitted values and direct sums, so we can work with the later.

Model modification

ggcorrplot(cor(predicted_and_direct_sums %>% 
                 select(-id, -PR, -CO, -UT, -FA, -DE, -UN, -DT)),
           type = "lower", lab = TRUE,
           colors = c("indianred1", "white", "royalblue1"))

modificationindices(model3) %>% arrange(desc(mi)) -> modif3

Measures:

modif3 %>% 
  filter(op == "=~") %>% 
  arrange(lhs) %>% 
  kable(digits = 2,
        col.names = c("Factor", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))
Factor Item Modification Index epc sepc.lv sepc.all sepc.nox
CO =~ fa06 96.43 0.62 0.49 0.46 0.46
CO =~ fa04 61.19 0.56 0.45 0.41 0.41
CO =~ fa02 46.88 -0.52 -0.41 -0.35 -0.35
CO =~ fa08 44.78 -0.50 -0.39 -0.35 -0.35
CO =~ ut03 34.67 -0.43 -0.34 -0.31 -0.31
CO =~ fa09 33.63 -0.43 -0.34 -0.29 -0.29
CO =~ ut11 26.27 0.41 0.32 0.26 0.26
CO =~ un07 22.01 0.26 0.21 0.18 0.18
CO =~ ut02 20.42 -0.22 -0.18 -0.17 -0.17
CO =~ ut01 18.65 -0.23 -0.18 -0.18 -0.18
CO =~ un10 9.29 0.16 0.13 0.11 0.11
CO =~ de05 7.82 -0.25 -0.20 -0.17 -0.17
CO =~ ut05 7.50 0.19 0.15 0.13 0.13
CO =~ ut08 7.42 0.17 0.14 0.13 0.13
CO =~ de09 5.24 -0.23 -0.19 -0.16 -0.16
CO =~ fa10 4.90 -0.15 -0.12 -0.10 -0.10
CO =~ pr09 4.84 -0.16 -0.13 -0.13 -0.13
CO =~ fa05 4.82 0.11 0.09 0.08 0.08
CO =~ de02 3.94 0.16 0.12 0.11 0.11
CO =~ un06 3.94 -0.14 -0.11 -0.09 -0.09
CO =~ ut04 3.52 -0.14 -0.11 -0.10 -0.10
CO =~ un11 3.15 -0.09 -0.07 -0.06 -0.06
CO =~ ut07 3.14 0.13 0.10 0.09 0.09
CO =~ ut12 3.12 0.11 0.09 0.08 0.08
CO =~ de03 2.76 0.15 0.12 0.10 0.10
CO =~ pr05 2.41 0.14 0.11 0.09 0.09
CO =~ de01 2.24 0.13 0.10 0.09 0.09
CO =~ ut09 1.95 0.10 0.08 0.06 0.06
CO =~ un12 1.92 -0.07 -0.06 -0.05 -0.05
CO =~ pr02 1.77 0.10 0.08 0.08 0.08
CO =~ pr07 1.73 0.10 0.08 0.07 0.07
CO =~ un01 1.60 -0.06 -0.05 -0.04 -0.04
CO =~ un09 1.60 0.07 0.06 0.05 0.05
CO =~ de08 1.43 -0.09 -0.07 -0.07 -0.07
CO =~ un08 1.19 -0.06 -0.04 -0.04 -0.04
CO =~ un05 0.99 -0.04 -0.03 -0.03 -0.03
CO =~ de10 0.79 0.07 0.06 0.05 0.05
CO =~ pr06 0.54 -0.06 -0.05 -0.04 -0.04
CO =~ fa01 0.39 -0.03 -0.03 -0.02 -0.02
CO =~ un04 0.18 0.02 0.02 0.01 0.01
CO =~ de07 0.11 -0.02 -0.02 -0.02 -0.02
CO =~ un02 0.10 -0.01 -0.01 -0.01 -0.01
CO =~ pr01 0.09 0.02 0.02 0.02 0.02
CO =~ pr08 0.08 -0.02 -0.01 -0.01 -0.01
CO =~ un03 0.01 -0.01 0.00 0.00 0.00
CO =~ ut06 0.01 0.01 0.00 0.00 0.00
CO =~ de06 0.00 -0.01 0.00 0.00 0.00
CO =~ pr10 0.00 0.00 0.00 0.00 0.00
DE =~ fa06 131.37 1.07 0.66 0.61 0.61
DE =~ ut11 77.11 1.13 0.70 0.56 0.56
DE =~ co03 66.49 0.84 0.52 0.51 0.51
DE =~ fa02 63.44 -0.89 -0.55 -0.46 -0.46
DE =~ co08 44.14 -0.75 -0.46 -0.44 -0.44
DE =~ co04 37.47 0.72 0.44 0.41 0.41
DE =~ ut01 37.12 -0.54 -0.33 -0.31 -0.31
DE =~ fa08 34.39 -0.64 -0.39 -0.34 -0.34
DE =~ fa09 33.97 -0.63 -0.39 -0.33 -0.33
DE =~ pr05 30.04 1.61 0.99 0.83 0.83
DE =~ co09 22.90 -0.45 -0.28 -0.26 -0.26
DE =~ fa04 22.02 0.49 0.30 0.28 0.28
DE =~ ut02 18.30 -0.35 -0.22 -0.20 -0.20
DE =~ un06 15.22 -0.36 -0.22 -0.18 -0.18
DE =~ ut12 12.92 0.38 0.23 0.20 0.20
DE =~ co01 11.78 0.32 0.20 0.18 0.18
DE =~ co05 10.33 -0.30 -0.19 -0.17 -0.17
DE =~ pr01 9.14 -0.65 -0.40 -0.41 -0.41
DE =~ fa10 7.55 -0.28 -0.17 -0.14 -0.14
DE =~ ut08 6.22 0.25 0.16 0.15 0.15
DE =~ ut03 5.98 -0.29 -0.18 -0.16 -0.16
DE =~ un07 5.76 0.18 0.11 0.09 0.09
DE =~ pr06 4.51 -0.55 -0.34 -0.32 -0.32
DE =~ fa05 4.41 0.17 0.10 0.09 0.09
DE =~ un11 3.96 -0.14 -0.08 -0.07 -0.07
DE =~ un09 3.57 0.15 0.09 0.07 0.07
DE =~ pr08 3.44 -0.38 -0.23 -0.26 -0.26
DE =~ un12 1.25 0.07 0.05 0.04 0.04
DE =~ pr07 1.22 0.28 0.17 0.15 0.15
DE =~ ut09 1.11 0.12 0.07 0.06 0.06
DE =~ co02 1.01 0.09 0.06 0.06 0.06
DE =~ un04 1.00 -0.06 -0.04 -0.04 -0.04
DE =~ pr10 0.91 -0.25 -0.15 -0.15 -0.15
DE =~ co06 0.90 -0.10 -0.06 -0.06 -0.06
DE =~ ut07 0.75 0.10 0.06 0.05 0.05
DE =~ un05 0.73 0.05 0.03 0.03 0.03
DE =~ un02 0.66 -0.05 -0.03 -0.03 -0.03
DE =~ ut06 0.60 0.07 0.04 0.04 0.04
DE =~ un01 0.29 0.03 0.02 0.02 0.02
DE =~ ut05 0.29 0.06 0.04 0.03 0.03
DE =~ pr02 0.16 0.10 0.06 0.06 0.06
DE =~ un03 0.12 0.03 0.02 0.02 0.02
DE =~ fa01 0.11 -0.03 -0.02 -0.01 -0.01
DE =~ ut04 0.06 0.03 0.02 0.02 0.02
DE =~ pr09 0.01 0.02 0.01 0.02 0.02
DE =~ co10 0.01 0.01 0.01 0.01 0.01
DE =~ un08 0.00 0.00 0.00 0.00 0.00
DE =~ un10 0.00 0.00 0.00 0.00 0.00
DT =~ fa06 139.75 0.95 0.71 0.65 0.65
DT =~ co03 79.85 0.82 0.61 0.59 0.59
DT =~ fa02 75.65 -0.84 -0.62 -0.53 -0.53
DT =~ ut11 71.86 0.98 0.73 0.59 0.59
DT =~ co04 62.54 0.82 0.61 0.56 0.56
DT =~ co08 60.53 -0.78 -0.57 -0.55 -0.55
DT =~ fa08 44.54 -0.62 -0.46 -0.41 -0.41
DT =~ fa09 38.52 -0.58 -0.43 -0.37 -0.37
DT =~ ut01 37.14 -0.48 -0.36 -0.34 -0.34
DT =~ co09 28.42 -0.44 -0.33 -0.31 -0.31
DT =~ de05 23.82 1.77 1.31 1.13 1.13
DT =~ pr05 23.22 2.61 1.93 1.61 1.61
DT =~ ut02 16.84 -0.30 -0.22 -0.21 -0.21
DT =~ un06 15.87 -0.30 -0.23 -0.18 -0.18
DT =~ fa04 15.80 0.36 0.27 0.25 0.25
DT =~ co05 13.51 -0.31 -0.23 -0.20 -0.20
DT =~ ut12 12.72 0.34 0.25 0.22 0.22
DT =~ co01 10.19 0.27 0.20 0.18 0.18
DT =~ ut08 8.64 0.27 0.20 0.19 0.19
DT =~ de10 8.29 -0.99 -0.73 -0.62 -0.62
DT =~ de07 7.14 0.81 0.60 0.60 0.60
DT =~ ut03 7.03 -0.28 -0.21 -0.19 -0.19
DT =~ de09 6.76 1.04 0.77 0.64 0.64
DT =~ fa10 6.35 -0.22 -0.17 -0.14 -0.14
DT =~ un11 4.86 -0.13 -0.09 -0.08 -0.08
DT =~ de03 4.67 -0.78 -0.58 -0.49 -0.49
DT =~ un07 3.71 0.12 0.09 0.08 0.08
DT =~ fa05 2.62 0.11 0.08 0.08 0.08
DT =~ de08 1.98 -0.44 -0.32 -0.31 -0.31
DT =~ pr01 1.97 -0.58 -0.43 -0.44 -0.44
DT =~ co02 1.83 0.11 0.08 0.08 0.08
DT =~ un01 1.77 0.07 0.05 0.05 0.05
DT =~ co06 1.66 -0.12 -0.09 -0.08 -0.08
DT =~ ut07 1.64 0.13 0.10 0.09 0.09
DT =~ pr08 1.52 -0.48 -0.35 -0.39 -0.39
DT =~ un12 1.50 0.07 0.05 0.05 0.05
DT =~ ut09 1.41 0.12 0.09 0.07 0.07
DT =~ un09 1.31 0.07 0.06 0.04 0.04
DT =~ un02 1.26 -0.05 -0.04 -0.04 -0.04
DT =~ de02 1.26 -0.38 -0.28 -0.24 -0.24
DT =~ pr10 1.22 -0.53 -0.39 -0.38 -0.38
DT =~ pr07 1.16 0.51 0.38 0.34 0.34
DT =~ un05 1.10 0.05 0.04 0.03 0.03
DT =~ pr09 0.89 -0.40 -0.30 -0.31 -0.31
DT =~ ut06 0.84 0.08 0.06 0.05 0.05
DT =~ pr06 0.47 -0.33 -0.25 -0.23 -0.23
DT =~ un04 0.41 -0.03 -0.03 -0.02 -0.02
DT =~ ut05 0.20 0.05 0.03 0.03 0.03
DT =~ ut04 0.20 -0.05 -0.04 -0.03 -0.03
DT =~ co10 0.17 0.04 0.03 0.03 0.03
DT =~ de01 0.12 0.12 0.09 0.08 0.08
DT =~ un03 0.09 0.02 0.02 0.01 0.01
DT =~ de06 0.09 0.11 0.08 0.07 0.07
DT =~ un10 0.07 -0.02 -0.01 -0.01 -0.01
DT =~ un08 0.03 0.01 0.01 0.01 0.01
DT =~ pr02 0.00 0.02 0.01 0.01 0.01
DT =~ fa01 0.00 0.00 0.00 0.00 0.00
FA =~ co08 21.44 -0.28 -0.25 -0.24 -0.24
FA =~ co03 19.45 0.24 0.22 0.22 0.22
FA =~ co01 17.95 0.20 0.19 0.17 0.17
FA =~ ut11 15.85 0.26 0.24 0.19 0.19
FA =~ co04 15.75 0.25 0.23 0.21 0.21
FA =~ un06 11.90 -0.20 -0.19 -0.15 -0.15
FA =~ pr10 10.43 -0.21 -0.19 -0.18 -0.18
FA =~ de10 9.04 0.18 0.17 0.14 0.14
FA =~ co06 8.52 -0.16 -0.15 -0.13 -0.13
FA =~ un03 8.16 -0.15 -0.14 -0.12 -0.12
FA =~ ut01 7.87 -0.12 -0.11 -0.11 -0.11
FA =~ co09 7.36 -0.13 -0.12 -0.11 -0.11
FA =~ co02 6.78 0.13 0.12 0.11 0.11
FA =~ de06 6.63 0.18 0.16 0.13 0.13
FA =~ pr06 5.63 -0.15 -0.14 -0.13 -0.13
FA =~ un01 4.88 0.09 0.08 0.08 0.08
FA =~ un11 4.13 -0.09 -0.08 -0.07 -0.07
FA =~ ut02 2.84 -0.07 -0.06 -0.06 -0.06
FA =~ pr05 2.81 0.12 0.11 0.09 0.09
FA =~ co10 2.63 -0.09 -0.08 -0.07 -0.07
FA =~ un05 2.32 0.06 0.05 0.05 0.05
FA =~ de05 2.25 -0.10 -0.10 -0.08 -0.08
FA =~ de03 2.05 0.10 0.09 0.08 0.08
FA =~ de07 1.72 -0.07 -0.07 -0.07 -0.07
FA =~ pr08 1.67 -0.06 -0.06 -0.06 -0.06
FA =~ pr02 1.53 -0.07 -0.06 -0.07 -0.07
FA =~ co05 1.34 -0.05 -0.05 -0.05 -0.05
FA =~ pr07 1.24 0.06 0.06 0.05 0.05
FA =~ ut03 1.17 0.06 0.06 0.05 0.05
FA =~ un04 1.15 -0.04 -0.04 -0.04 -0.04
FA =~ un10 0.80 -0.04 -0.04 -0.03 -0.03
FA =~ ut04 0.64 0.05 0.04 0.04 0.04
FA =~ ut08 0.57 0.04 0.04 0.03 0.03
FA =~ un07 0.56 0.04 0.03 0.03 0.03
FA =~ ut07 0.54 0.04 0.04 0.03 0.03
FA =~ un08 0.53 0.03 0.03 0.02 0.02
FA =~ pr09 0.45 -0.04 -0.03 -0.04 -0.04
FA =~ de02 0.42 0.04 0.04 0.03 0.03
FA =~ un02 0.35 -0.02 -0.02 -0.02 -0.02
FA =~ ut06 0.33 0.03 0.03 0.02 0.02
FA =~ ut09 0.24 -0.03 -0.03 -0.02 -0.02
FA =~ ut05 0.09 0.02 0.02 0.01 0.01
FA =~ de09 0.09 -0.02 -0.02 -0.02 -0.02
FA =~ un12 0.06 -0.01 -0.01 -0.01 -0.01
FA =~ de08 0.03 0.01 0.01 0.01 0.01
FA =~ de01 0.02 0.01 0.01 0.01 0.01
FA =~ ut12 0.00 0.00 0.00 0.00 0.00
FA =~ pr01 0.00 0.00 0.00 0.00 0.00
FA =~ un09 0.00 0.00 0.00 0.00 0.00
PR =~ fa06 134.64 0.87 0.67 0.62 0.62
PR =~ co03 77.18 0.74 0.57 0.56 0.56
PR =~ fa02 73.45 -0.77 -0.59 -0.50 -0.50
PR =~ ut11 62.90 0.83 0.64 0.52 0.52
PR =~ co04 62.73 0.75 0.58 0.53 0.53
PR =~ co08 59.54 -0.70 -0.54 -0.52 -0.52
PR =~ fa08 41.01 -0.56 -0.43 -0.38 -0.38
PR =~ fa09 34.42 -0.51 -0.40 -0.34 -0.34
PR =~ ut01 34.08 -0.42 -0.32 -0.31 -0.31
PR =~ co09 25.92 -0.39 -0.30 -0.28 -0.28
PR =~ de05 23.16 1.12 0.87 0.74 0.74
PR =~ un06 15.26 -0.28 -0.22 -0.18 -0.18
PR =~ ut12 12.98 0.31 0.24 0.21 0.21
PR =~ ut02 12.92 -0.24 -0.18 -0.18 -0.18
PR =~ co05 11.98 -0.26 -0.20 -0.18 -0.18
PR =~ fa04 11.62 0.29 0.22 0.20 0.20
PR =~ de10 11.52 -0.74 -0.57 -0.49 -0.49
PR =~ ut08 9.63 0.26 0.20 0.19 0.19
PR =~ de09 7.51 0.71 0.54 0.45 0.45
PR =~ co01 7.27 0.21 0.16 0.15 0.15
PR =~ de03 6.68 -0.60 -0.46 -0.39 -0.39
PR =~ fa10 6.31 -0.21 -0.16 -0.13 -0.13
PR =~ ut03 4.84 -0.21 -0.16 -0.15 -0.15
PR =~ un11 4.50 -0.12 -0.09 -0.07 -0.07
PR =~ de07 4.43 0.41 0.31 0.31 0.31
PR =~ un07 2.45 0.09 0.07 0.06 0.06
PR =~ co02 2.40 0.12 0.09 0.09 0.09
PR =~ un01 2.35 0.08 0.06 0.06 0.06
PR =~ de08 2.21 -0.29 -0.23 -0.21 -0.21
PR =~ ut07 1.97 0.13 0.10 0.09 0.09
PR =~ co06 1.82 -0.12 -0.09 -0.08 -0.08
PR =~ un12 1.79 0.07 0.06 0.05 0.05
PR =~ un02 1.60 -0.06 -0.05 -0.04 -0.04
PR =~ ut09 1.59 0.11 0.09 0.08 0.08
PR =~ un05 1.38 0.06 0.04 0.04 0.04
PR =~ fa05 1.25 0.07 0.06 0.05 0.05
PR =~ ut06 1.20 0.08 0.07 0.06 0.06
PR =~ un09 0.77 0.05 0.04 0.03 0.03
PR =~ ut04 0.56 -0.07 -0.06 -0.05 -0.05
PR =~ co10 0.41 0.05 0.04 0.04 0.04
PR =~ de02 0.28 -0.11 -0.09 -0.08 -0.08
PR =~ un04 0.20 -0.02 -0.02 -0.02 -0.02
PR =~ un10 0.09 -0.02 -0.01 -0.01 -0.01
PR =~ un08 0.08 0.02 0.01 0.01 0.01
PR =~ un03 0.07 0.02 0.01 0.01 0.01
PR =~ fa01 0.01 0.01 0.01 0.00 0.00
PR =~ de06 0.00 0.01 0.01 0.01 0.01
PR =~ de01 0.00 0.01 0.01 0.00 0.00
PR =~ ut05 0.00 0.00 0.00 0.00 0.00
UN =~ fa02 56.03 -0.51 -0.40 -0.34 -0.34
UN =~ fa08 47.24 -0.45 -0.35 -0.31 -0.31
UN =~ fa09 36.97 -0.40 -0.31 -0.26 -0.26
UN =~ fa06 28.05 0.30 0.23 0.21 0.21
UN =~ co10 16.84 0.23 0.18 0.17 0.17
UN =~ co08 14.61 -0.24 -0.18 -0.18 -0.18
UN =~ ut11 11.47 0.23 0.18 0.14 0.14
UN =~ ut03 10.84 -0.20 -0.16 -0.14 -0.14
UN =~ de07 9.76 0.17 0.13 0.13 0.13
UN =~ co04 8.55 0.19 0.15 0.14 0.14
UN =~ de10 8.50 -0.17 -0.13 -0.11 -0.11
UN =~ co05 7.66 -0.13 -0.11 -0.09 -0.09
UN =~ co09 7.08 -0.13 -0.10 -0.10 -0.10
UN =~ pr02 6.66 0.14 0.11 0.11 0.11
UN =~ fa10 5.93 -0.15 -0.12 -0.10 -0.10
UN =~ pr07 4.32 -0.11 -0.09 -0.08 -0.08
UN =~ ut12 4.00 0.11 0.09 0.07 0.07
UN =~ fa04 3.95 0.13 0.10 0.09 0.09
UN =~ co03 3.50 0.11 0.08 0.08 0.08
UN =~ de06 1.89 0.09 0.07 0.06 0.06
UN =~ de08 1.65 0.07 0.05 0.05 0.05
UN =~ de01 1.43 0.07 0.06 0.05 0.05
UN =~ pr06 1.41 0.07 0.05 0.05 0.05
UN =~ de05 1.18 0.07 0.06 0.05 0.05
UN =~ co02 1.13 0.05 0.04 0.04 0.04
UN =~ ut06 0.81 -0.04 -0.03 -0.03 -0.03
UN =~ fa01 0.67 0.04 0.03 0.03 0.03
UN =~ pr10 0.65 0.05 0.04 0.04 0.04
UN =~ fa05 0.56 0.03 0.03 0.02 0.02
UN =~ pr05 0.55 0.05 0.04 0.03 0.03
UN =~ co06 0.53 0.04 0.03 0.03 0.03
UN =~ pr08 0.50 -0.03 -0.02 -0.03 -0.03
UN =~ de02 0.50 -0.04 -0.03 -0.03 -0.03
UN =~ ut05 0.40 0.04 0.03 0.02 0.02
UN =~ ut04 0.37 -0.04 -0.03 -0.03 -0.03
UN =~ de09 0.37 -0.05 -0.04 -0.03 -0.03
UN =~ ut01 0.33 -0.03 -0.02 -0.02 -0.02
UN =~ de03 0.27 -0.03 -0.03 -0.02 -0.02
UN =~ ut07 0.27 -0.03 -0.02 -0.02 -0.02
UN =~ pr01 0.11 0.01 0.01 0.01 0.01
UN =~ co01 0.10 0.02 0.01 0.01 0.01
UN =~ ut02 0.07 -0.01 -0.01 -0.01 -0.01
UN =~ pr09 0.02 -0.01 -0.01 -0.01 -0.01
UN =~ ut08 0.01 0.01 0.00 0.00 0.00
UN =~ ut09 0.01 0.00 0.00 0.00 0.00
UT =~ co04 112.78 0.78 0.63 0.58 0.58
UT =~ co03 71.66 0.55 0.44 0.43 0.43
UT =~ co08 58.36 -0.54 -0.44 -0.42 -0.42
UT =~ de05 52.89 0.67 0.54 0.47 0.47
UT =~ fa06 47.51 0.42 0.34 0.32 0.32
UT =~ fa02 37.75 -0.46 -0.37 -0.31 -0.31
UT =~ co09 29.78 -0.31 -0.26 -0.24 -0.24
UT =~ fa08 28.61 -0.38 -0.31 -0.28 -0.28
UT =~ de02 20.41 -0.36 -0.30 -0.26 -0.26
UT =~ co05 19.51 -0.25 -0.20 -0.18 -0.18
UT =~ fa09 19.11 -0.31 -0.26 -0.22 -0.22
UT =~ pr06 18.86 0.36 0.29 0.28 0.28
UT =~ de09 17.71 0.43 0.35 0.29 0.29
UT =~ un01 16.59 0.18 0.15 0.14 0.14
UT =~ de07 14.55 0.29 0.23 0.23 0.23
UT =~ un06 13.77 -0.25 -0.20 -0.16 -0.16
UT =~ un10 10.65 -0.17 -0.14 -0.12 -0.12
UT =~ un12 8.85 0.14 0.12 0.10 0.10
UT =~ de03 7.09 -0.24 -0.19 -0.16 -0.16
UT =~ un11 6.12 -0.12 -0.10 -0.08 -0.08
UT =~ un03 5.78 0.15 0.12 0.10 0.10
UT =~ de10 5.42 -0.19 -0.15 -0.13 -0.13
UT =~ pr02 4.21 -0.15 -0.13 -0.13 -0.13
UT =~ pr08 3.96 0.13 0.10 0.11 0.11
UT =~ pr01 3.87 0.13 0.10 0.10 0.10
UT =~ co01 3.54 0.11 0.09 0.08 0.08
UT =~ fa04 3.16 -0.12 -0.10 -0.09 -0.09
UT =~ un02 2.69 -0.07 -0.06 -0.05 -0.05
UT =~ un05 2.66 0.07 0.06 0.05 0.05
UT =~ de06 1.82 -0.12 -0.10 -0.08 -0.08
UT =~ un08 1.31 0.06 0.05 0.04 0.04
UT =~ pr05 1.12 -0.10 -0.08 -0.07 -0.07
UT =~ fa01 0.93 0.05 0.04 0.04 0.04
UT =~ pr10 0.86 0.08 0.06 0.06 0.06
UT =~ un07 0.64 -0.04 -0.03 -0.03 -0.03
UT =~ fa05 0.60 0.04 0.03 0.03 0.03
UT =~ un09 0.49 -0.04 -0.03 -0.03 -0.03
UT =~ co02 0.47 -0.04 -0.03 -0.03 -0.03
UT =~ de01 0.40 -0.05 -0.04 -0.04 -0.04
UT =~ fa10 0.35 0.04 0.03 0.03 0.03
UT =~ pr07 0.27 -0.04 -0.03 -0.03 -0.03
UT =~ un04 0.24 -0.02 -0.02 -0.02 -0.02
UT =~ co06 0.23 -0.03 -0.03 -0.02 -0.02
UT =~ de08 0.20 -0.03 -0.03 -0.03 -0.03
UT =~ pr09 0.10 -0.02 -0.02 -0.02 -0.02
UT =~ co10 0.01 0.00 0.00 0.00 0.00

Items correlations:

modif3 %>% 
  filter(op == "~~" & 
           !(lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) &
           !(rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  kable(digits = 2,
        col.names = c("Item", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))
Item Item Modification Index epc sepc.lv sepc.all sepc.nox
de05 ~~ de09 182.77 0.70 0.70 0.63 0.63
pr05 ~~ de06 168.01 0.58 0.58 0.62 0.62
fa02 ~~ fa08 132.56 0.60 0.60 0.53 0.53
pr05 ~~ ut11 125.45 0.54 0.54 0.52 0.52
fa02 ~~ fa09 118.25 0.57 0.57 0.51 0.51
fa08 ~~ fa09 105.17 0.52 0.52 0.48 0.48
ut11 ~~ de06 100.33 0.47 0.47 0.47 0.47
ut03 ~~ de09 59.71 0.39 0.39 0.35 0.35
pr06 ~~ ut06 58.50 0.24 0.24 0.37 0.37
ut01 ~~ ut02 55.84 0.17 0.17 0.44 0.44
fa02 ~~ fa06 51.07 -0.32 -0.32 -0.34 -0.34
co08 ~~ ut03 44.59 -0.29 -0.29 -0.31 -0.31
co08 ~~ co09 43.60 0.24 0.24 0.33 0.33
ut03 ~~ fa04 41.73 -0.29 -0.29 -0.30 -0.30
co04 ~~ co08 41.47 -0.29 -0.29 -0.29 -0.29
ut07 ~~ ut08 40.80 0.23 0.23 0.30 0.30
co08 ~~ de09 40.13 -0.32 -0.32 -0.29 -0.29
co03 ~~ co04 37.93 0.26 0.26 0.29 0.29
co04 ~~ de10 37.10 -0.25 -0.25 -0.29 -0.29
ut06 ~~ de07 36.79 0.17 0.17 0.30 0.30
pr06 ~~ de07 35.62 0.20 0.20 0.28 0.28
co05 ~~ co09 33.35 0.18 0.18 0.34 0.34
fa04 ~~ un07 32.68 0.23 0.23 0.27 0.27
un01 ~~ un08 32.62 0.15 0.15 0.29 0.29
co04 ~~ un01 29.62 0.18 0.18 0.26 0.26
pr08 ~~ de05 29.56 0.16 0.16 0.26 0.26
pr09 ~~ de10 26.09 -0.16 -0.16 -0.25 -0.25
fa04 ~~ de05 25.91 -0.24 -0.24 -0.24 -0.24
fa09 ~~ un03 24.74 -0.24 -0.24 -0.23 -0.23
pr08 ~~ de06 24.58 -0.15 -0.15 -0.24 -0.24
pr09 ~~ ut08 24.00 0.15 0.15 0.23 0.23
co08 ~~ fa04 23.88 0.22 0.22 0.22 0.22
fa04 ~~ de03 23.44 0.22 0.22 0.23 0.23
fa04 ~~ un10 23.32 0.19 0.19 0.23 0.23
co08 ~~ un07 22.48 0.19 0.19 0.22 0.22
co10 ~~ ut03 22.37 -0.19 -0.19 -0.22 -0.22
co03 ~~ co08 21.95 -0.19 -0.19 -0.22 -0.22
co08 ~~ un01 21.84 -0.15 -0.15 -0.22 -0.22
fa02 ~~ fa10 21.72 0.23 0.23 0.22 0.22
fa04 ~~ fa08 21.49 -0.23 -0.23 -0.21 -0.21
fa06 ~~ fa08 21.28 -0.20 -0.20 -0.22 -0.22
co03 ~~ un10 21.24 -0.16 -0.16 -0.22 -0.22
ut02 ~~ un10 21.21 -0.11 -0.11 -0.24 -0.24
fa06 ~~ fa10 21.14 -0.19 -0.19 -0.23 -0.23
fa04 ~~ de09 21.00 -0.24 -0.24 -0.21 -0.21
de05 ~~ un07 20.82 -0.18 -0.18 -0.22 -0.22
fa04 ~~ fa09 20.42 -0.22 -0.22 -0.21 -0.21
co04 ~~ co05 20.40 -0.16 -0.16 -0.23 -0.23
fa04 ~~ un01 19.90 -0.15 -0.15 -0.21 -0.21
co08 ~~ de10 19.79 0.17 0.17 0.21 0.21
fa09 ~~ un06 19.23 0.23 0.23 0.20 0.20
co04 ~~ ut01 18.96 0.14 0.14 0.21 0.21
pr07 ~~ de02 18.78 0.14 0.14 0.22 0.22
de05 ~~ un05 18.63 0.14 0.14 0.21 0.21
co03 ~~ de07 18.33 0.14 0.14 0.20 0.20
fa02 ~~ un03 18.30 -0.21 -0.21 -0.20 -0.20
co09 ~~ de05 18.28 -0.15 -0.15 -0.21 -0.21
fa04 ~~ un12 18.25 -0.16 -0.16 -0.20 -0.20
ut03 ~~ fa09 18.22 0.20 0.20 0.20 0.20
co04 ~~ un07 18.15 -0.17 -0.17 -0.20 -0.20
fa06 ~~ fa09 18.03 -0.18 -0.18 -0.20 -0.20
ut01 ~~ de05 18.00 0.14 0.14 0.21 0.21
co04 ~~ de05 17.94 0.20 0.20 0.20 0.20
ut11 ~~ un09 17.94 0.19 0.19 0.20 0.20
co05 ~~ co08 17.89 0.15 0.15 0.22 0.22
co04 ~~ co09 17.73 -0.16 -0.16 -0.21 -0.21
de09 ~~ un07 17.50 -0.19 -0.19 -0.19 -0.19
de05 ~~ un10 17.02 -0.16 -0.16 -0.20 -0.20
fa04 ~~ un08 16.99 -0.15 -0.15 -0.20 -0.20
de05 ~~ un12 16.99 0.15 0.15 0.20 0.20
co08 ~~ fa09 16.13 -0.19 -0.19 -0.19 -0.19
fa08 ~~ un03 15.93 -0.19 -0.19 -0.18 -0.18
ut01 ~~ fa04 15.86 -0.13 -0.13 -0.19 -0.19
co09 ~~ fa04 15.60 0.14 0.14 0.19 0.19
pr01 ~~ un01 15.38 0.09 0.09 0.20 0.20
fa10 ~~ un08 15.35 0.14 0.14 0.19 0.19
pr05 ~~ de01 15.31 -0.16 -0.16 -0.18 -0.18
ut01 ~~ un07 15.31 -0.11 -0.11 -0.19 -0.19
co04 ~~ ut03 15.13 0.18 0.18 0.18 0.18
ut01 ~~ ut03 15.09 0.12 0.12 0.19 0.19
co04 ~~ fa04 15.01 -0.18 -0.18 -0.18 -0.18
co06 ~~ un10 14.83 0.13 0.13 0.19 0.19
un07 ~~ un09 14.63 0.14 0.14 0.18 0.18
ut03 ~~ de05 14.57 0.17 0.17 0.18 0.18
co08 ~~ de05 14.56 -0.17 -0.17 -0.18 -0.18
de01 ~~ de09 14.39 -0.18 -0.18 -0.18 -0.18
un01 ~~ un10 14.28 -0.11 -0.11 -0.19 -0.19
co04 ~~ ut04 14.14 0.17 0.17 0.17 0.17
fa02 ~~ fa04 14.14 -0.19 -0.19 -0.17 -0.17
un07 ~~ un10 14.10 0.13 0.13 0.18 0.18
pr09 ~~ de01 13.97 0.12 0.12 0.18 0.18
fa10 ~~ un07 13.93 -0.15 -0.15 -0.18 -0.18
fa04 ~~ de02 13.83 0.15 0.15 0.18 0.18
pr08 ~~ de09 13.70 0.13 0.13 0.18 0.18
fa09 ~~ de09 13.29 0.20 0.20 0.17 0.17
pr08 ~~ co03 13.19 0.10 0.10 0.18 0.18
pr05 ~~ de05 13.00 -0.16 -0.16 -0.17 -0.17
ut03 ~~ fa08 12.94 0.17 0.17 0.17 0.17
fa06 ~~ un08 12.84 -0.12 -0.12 -0.17 -0.17
de02 ~~ un01 12.71 -0.10 -0.10 -0.18 -0.18
fa04 ~~ de10 12.65 0.14 0.14 0.17 0.17
de03 ~~ de10 12.61 0.14 0.14 0.18 0.18
ut01 ~~ un09 12.54 -0.10 -0.10 -0.18 -0.18
ut03 ~~ un10 12.50 -0.13 -0.13 -0.17 -0.17
co06 ~~ un01 12.38 -0.11 -0.11 -0.17 -0.17
co01 ~~ co06 12.28 -0.12 -0.12 -0.19 -0.19
pr09 ~~ ut12 12.14 0.11 0.11 0.17 0.17
de03 ~~ de05 12.11 -0.16 -0.16 -0.17 -0.17
de01 ~~ de06 12.08 -0.15 -0.15 -0.17 -0.17
ut01 ~~ ut11 12.07 -0.12 -0.12 -0.17 -0.17
fa09 ~~ un07 11.93 -0.14 -0.14 -0.16 -0.16
co04 ~~ un10 11.92 -0.13 -0.13 -0.16 -0.16
co08 ~~ un10 11.81 0.13 0.13 0.16 0.16
de03 ~~ de09 11.78 -0.17 -0.17 -0.16 -0.16
fa09 ~~ fa10 11.75 0.16 0.16 0.16 0.16
de09 ~~ de10 11.64 -0.16 -0.16 -0.16 -0.16
ut05 ~~ un10 11.63 0.12 0.12 0.16 0.16
co04 ~~ un05 11.63 0.11 0.11 0.17 0.17
co04 ~~ de09 11.55 0.18 0.18 0.15 0.15
un01 ~~ un05 11.54 0.08 0.08 0.18 0.18
de05 ~~ de10 11.50 -0.14 -0.14 -0.17 -0.17
ut02 ~~ un12 11.44 0.08 0.08 0.18 0.18
pr05 ~~ un01 11.41 -0.11 -0.11 -0.16 -0.16
co06 ~~ fa04 11.20 0.14 0.14 0.16 0.16
pr08 ~~ ut02 11.18 0.06 0.06 0.18 0.18
co10 ~~ fa08 11.16 -0.14 -0.14 -0.16 -0.16
un02 ~~ un06 11.11 0.11 0.11 0.17 0.17
pr01 ~~ un04 11.08 0.08 0.08 0.17 0.17
co10 ~~ un10 11.07 0.11 0.11 0.16 0.16
pr01 ~~ co08 11.01 -0.10 -0.10 -0.16 -0.16
co05 ~~ ut01 10.94 -0.08 -0.08 -0.18 -0.18
pr01 ~~ de10 10.93 -0.09 -0.09 -0.17 -0.17
de09 ~~ un06 10.79 0.18 0.18 0.15 0.15
ut02 ~~ ut03 10.62 0.10 0.10 0.17 0.17
ut03 ~~ fa02 10.62 0.16 0.16 0.15 0.15
de09 ~~ un05 10.57 0.12 0.12 0.16 0.16
pr07 ~~ ut09 10.47 0.11 0.11 0.16 0.16
ut08 ~~ de01 10.41 0.11 0.11 0.15 0.15
pr01 ~~ pr09 10.40 0.08 0.08 0.17 0.17
co09 ~~ ut03 10.15 -0.11 -0.11 -0.16 -0.16
pr01 ~~ ut03 10.12 0.09 0.09 0.16 0.16
de10 ~~ un08 10.11 -0.10 -0.10 -0.16 -0.16
fa06 ~~ de02 10.07 0.11 0.11 0.16 0.16
fa08 ~~ fa10 10.03 0.15 0.15 0.15 0.15
un05 ~~ un09 9.91 -0.09 -0.09 -0.16 -0.16
pr07 ~~ de06 9.90 -0.11 -0.11 -0.16 -0.16
ut03 ~~ un06 9.84 0.15 0.15 0.14 0.14
ut02 ~~ de05 9.82 0.09 0.09 0.16 0.16
ut01 ~~ fa10 9.66 0.10 0.10 0.16 0.16
co06 ~~ un09 9.62 0.12 0.12 0.15 0.15
ut08 ~~ ut11 9.59 -0.13 -0.13 -0.15 -0.15
un08 ~~ un11 9.53 0.09 0.09 0.16 0.16
co08 ~~ un05 9.50 -0.09 -0.09 -0.15 -0.15
pr09 ~~ un11 9.49 -0.09 -0.09 -0.15 -0.15
de08 ~~ un04 9.36 -0.08 -0.08 -0.15 -0.15
fa06 ~~ un10 9.21 0.10 0.10 0.15 0.15
fa02 ~~ un06 9.20 0.16 0.16 0.14 0.14
pr06 ~~ de03 9.08 -0.12 -0.12 -0.14 -0.14
co05 ~~ un04 9.05 0.08 0.08 0.16 0.16
pr06 ~~ un08 8.90 -0.10 -0.10 -0.14 -0.14
de02 ~~ de05 8.90 -0.12 -0.12 -0.15 -0.15
co04 ~~ un12 8.90 0.11 0.11 0.14 0.14
fa04 ~~ fa06 8.87 0.13 0.13 0.14 0.14
de10 ~~ un01 8.79 -0.09 -0.09 -0.15 -0.15
fa05 ~~ de09 8.77 -0.11 -0.11 -0.16 -0.16
pr05 ~~ un09 8.75 0.12 0.12 0.14 0.14
un02 ~~ un12 8.74 -0.07 -0.07 -0.16 -0.16
co04 ~~ de02 8.58 -0.12 -0.12 -0.14 -0.14
co03 ~~ co05 8.49 -0.09 -0.09 -0.15 -0.15
ut02 ~~ ut12 8.49 -0.08 -0.08 -0.16 -0.16
de09 ~~ un10 8.47 -0.13 -0.13 -0.14 -0.14
de05 ~~ un08 8.44 0.10 0.10 0.14 0.14
co06 ~~ ut09 8.43 0.11 0.11 0.14 0.14
pr01 ~~ fa06 8.37 0.08 0.08 0.15 0.15
pr06 ~~ de02 8.31 -0.10 -0.10 -0.14 -0.14
pr01 ~~ fa04 8.29 -0.09 -0.09 -0.14 -0.14
co08 ~~ ut02 8.29 -0.08 -0.08 -0.15 -0.15
pr08 ~~ un07 8.25 -0.08 -0.08 -0.14 -0.14
pr09 ~~ un05 8.21 0.07 0.07 0.14 0.14
ut01 ~~ ut09 8.19 -0.09 -0.09 -0.15 -0.15
fa04 ~~ fa10 8.19 -0.13 -0.13 -0.14 -0.14
co03 ~~ fa04 8.17 -0.12 -0.12 -0.13 -0.13
pr05 ~~ ut12 8.15 -0.11 -0.11 -0.14 -0.14
de06 ~~ de10 8.14 0.11 0.11 0.14 0.14
co03 ~~ de03 8.12 -0.11 -0.11 -0.13 -0.13
co03 ~~ ut03 8.10 0.11 0.11 0.13 0.13
pr08 ~~ de03 8.04 -0.08 -0.08 -0.14 -0.14
pr05 ~~ de10 7.93 0.11 0.11 0.14 0.14
co09 ~~ un07 7.88 0.09 0.09 0.14 0.14
ut11 ~~ fa06 7.81 0.12 0.12 0.13 0.13
fa01 ~~ un01 7.75 0.07 0.07 0.15 0.15
un01 ~~ un07 7.72 -0.08 -0.08 -0.14 -0.14
co04 ~~ un08 7.69 0.10 0.10 0.13 0.13
ut09 ~~ un09 7.63 0.10 0.10 0.13 0.13
fa02 ~~ un10 7.62 -0.11 -0.11 -0.13 -0.13
fa01 ~~ un10 7.62 -0.07 -0.07 -0.15 -0.15
de03 ~~ un10 7.62 0.10 0.10 0.13 0.13
pr01 ~~ pr05 7.61 -0.09 -0.09 -0.14 -0.14
ut01 ~~ un01 7.60 0.06 0.06 0.14 0.14
fa10 ~~ un01 7.58 0.09 0.09 0.13 0.13
ut11 ~~ de05 7.58 -0.13 -0.13 -0.13 -0.13
pr05 ~~ ut01 7.56 -0.09 -0.09 -0.14 -0.14
un02 ~~ un03 7.43 0.08 0.08 0.14 0.14
fa10 ~~ de02 7.43 -0.11 -0.11 -0.13 -0.13
ut01 ~~ fa06 7.42 -0.08 -0.08 -0.14 -0.14
de03 ~~ de06 7.40 0.12 0.12 0.13 0.13
ut02 ~~ fa04 7.36 -0.08 -0.08 -0.14 -0.14
ut04 ~~ ut07 7.36 -0.12 -0.12 -0.13 -0.13
co09 ~~ un12 7.27 -0.08 -0.08 -0.14 -0.14
co09 ~~ un06 7.24 0.10 0.10 0.13 0.13
un02 ~~ un04 7.23 0.07 0.07 0.14 0.14
ut01 ~~ ut05 7.15 0.08 0.08 0.14 0.14
co01 ~~ ut12 7.07 0.08 0.08 0.13 0.13
ut07 ~~ de06 7.05 -0.11 -0.11 -0.13 -0.13
pr09 ~~ fa04 7.03 -0.10 -0.10 -0.12 -0.12
co03 ~~ un08 6.77 0.08 0.08 0.12 0.12
co03 ~~ ut11 6.76 0.11 0.11 0.12 0.12
pr09 ~~ ut11 6.75 -0.10 -0.10 -0.12 -0.12
co04 ~~ un06 6.74 -0.13 -0.13 -0.12 -0.12
pr08 ~~ fa08 6.71 -0.08 -0.08 -0.12 -0.12
pr02 ~~ ut03 6.69 -0.09 -0.09 -0.12 -0.12
ut11 ~~ de01 6.69 -0.12 -0.12 -0.12 -0.12
pr06 ~~ co04 6.67 0.11 0.11 0.12 0.12
fa05 ~~ fa09 6.64 -0.09 -0.09 -0.15 -0.15
pr02 ~~ de06 6.61 0.09 0.09 0.12 0.12
co01 ~~ de10 6.59 0.08 0.08 0.13 0.13
de10 ~~ un09 6.56 0.09 0.09 0.13 0.13
ut11 ~~ fa04 6.52 0.13 0.13 0.12 0.12
co04 ~~ de07 6.50 0.10 0.10 0.12 0.12
co08 ~~ ut05 6.49 0.11 0.11 0.12 0.12
fa04 ~~ un09 6.44 0.11 0.11 0.12 0.12
pr08 ~~ un02 6.42 -0.05 -0.05 -0.13 -0.13
ut02 ~~ ut04 6.37 0.07 0.07 0.13 0.13
un05 ~~ un07 6.33 -0.07 -0.07 -0.13 -0.13
ut12 ~~ de03 6.33 0.10 0.10 0.12 0.12
ut02 ~~ un03 6.32 0.07 0.07 0.13 0.13
co05 ~~ de05 6.26 -0.09 -0.09 -0.13 -0.13
co09 ~~ de06 6.25 0.09 0.09 0.13 0.13
pr06 ~~ ut11 6.25 -0.11 -0.11 -0.12 -0.12
fa10 ~~ un02 6.23 -0.08 -0.08 -0.13 -0.13
co03 ~~ de05 6.17 0.10 0.10 0.12 0.12
ut01 ~~ de02 6.10 -0.07 -0.07 -0.13 -0.13
pr09 ~~ ut07 6.07 0.08 0.08 0.12 0.12
fa08 ~~ de08 6.04 0.09 0.09 0.12 0.12
pr05 ~~ co04 6.03 -0.11 -0.11 -0.11 -0.11
ut06 ~~ de02 6.03 -0.07 -0.07 -0.12 -0.12
fa08 ~~ un06 6.00 0.13 0.13 0.11 0.11
fa09 ~~ de07 6.00 -0.10 -0.10 -0.12 -0.12
de03 ~~ un12 5.96 -0.09 -0.09 -0.12 -0.12
co08 ~~ un11 5.95 0.09 0.09 0.12 0.12
co02 ~~ ut09 5.93 -0.08 -0.08 -0.12 -0.12
ut07 ~~ ut09 5.92 -0.10 -0.10 -0.12 -0.12
co03 ~~ de08 5.92 0.08 0.08 0.12 0.12
pr05 ~~ ut08 5.91 -0.09 -0.09 -0.12 -0.12
pr01 ~~ co04 5.85 0.07 0.07 0.12 0.12
de06 ~~ un01 5.84 -0.08 -0.08 -0.12 -0.12
co08 ~~ de03 5.81 0.10 0.10 0.11 0.11
de03 ~~ un02 5.79 0.07 0.07 0.12 0.12
co06 ~~ ut02 5.78 -0.06 -0.06 -0.13 -0.13
de06 ~~ un09 5.76 0.10 0.10 0.12 0.12
ut03 ~~ un07 5.74 -0.09 -0.09 -0.11 -0.11
co01 ~~ un01 5.73 0.06 0.06 0.12 0.12
ut01 ~~ un05 5.71 0.05 0.05 0.12 0.12
ut03 ~~ de01 5.70 -0.10 -0.10 -0.11 -0.11
pr05 ~~ fa08 5.68 0.11 0.11 0.11 0.11
pr10 ~~ ut04 5.68 -0.10 -0.10 -0.11 -0.11
ut05 ~~ de10 5.68 0.09 0.09 0.12 0.12
un08 ~~ un10 5.67 -0.07 -0.07 -0.12 -0.12
de05 ~~ un11 5.66 -0.09 -0.09 -0.11 -0.11
de05 ~~ un06 5.66 -0.12 -0.12 -0.11 -0.11
ut02 ~~ un07 5.64 -0.06 -0.06 -0.12 -0.12
un08 ~~ un09 5.63 -0.08 -0.08 -0.12 -0.12
co09 ~~ de02 5.61 0.07 0.07 0.12 0.12
co05 ~~ un11 5.59 -0.07 -0.07 -0.12 -0.12
ut09 ~~ ut11 5.58 0.10 0.10 0.11 0.11
fa08 ~~ de09 5.57 0.13 0.13 0.11 0.11
co01 ~~ fa05 5.57 0.06 0.06 0.14 0.14
co06 ~~ co10 5.54 0.09 0.09 0.12 0.12
ut07 ~~ fa08 5.53 -0.11 -0.11 -0.11 -0.11
de02 ~~ un07 5.52 0.08 0.08 0.11 0.11
pr05 ~~ un07 5.46 0.09 0.09 0.11 0.11
ut11 ~~ de10 5.45 0.10 0.10 0.11 0.11
ut04 ~~ un06 5.42 -0.11 -0.11 -0.11 -0.11
co08 ~~ fa08 5.41 -0.11 -0.11 -0.11 -0.11
co04 ~~ fa02 5.41 -0.12 -0.12 -0.11 -0.11
pr05 ~~ un05 5.41 -0.07 -0.07 -0.11 -0.11
co06 ~~ fa10 5.39 -0.09 -0.09 -0.11 -0.11
pr09 ~~ co04 5.38 0.08 0.08 0.11 0.11
ut12 ~~ de01 5.38 0.08 0.08 0.11 0.11
co08 ~~ un06 5.26 -0.11 -0.11 -0.11 -0.11
co01 ~~ ut04 5.22 -0.08 -0.08 -0.11 -0.11
ut01 ~~ un03 5.22 0.07 0.07 0.11 0.11
pr09 ~~ de08 5.21 0.07 0.07 0.11 0.11
co09 ~~ ut04 5.19 -0.08 -0.08 -0.11 -0.11
pr05 ~~ pr08 5.17 -0.07 -0.07 -0.11 -0.11
co08 ~~ co10 5.15 0.09 0.09 0.11 0.11
ut05 ~~ un07 5.15 0.08 0.08 0.11 0.11
pr02 ~~ pr05 5.15 0.08 0.08 0.11 0.11
co04 ~~ fa10 5.12 0.10 0.10 0.11 0.11
co01 ~~ de01 5.09 0.07 0.07 0.11 0.11
ut08 ~~ de10 5.06 -0.07 -0.07 -0.11 -0.11
pr05 ~~ ut07 5.05 -0.10 -0.10 -0.11 -0.11
co08 ~~ fa05 5.05 0.07 0.07 0.12 0.12
pr07 ~~ ut01 5.04 -0.06 -0.06 -0.11 -0.11
pr10 ~~ co06 5.04 -0.08 -0.08 -0.11 -0.11
co03 ~~ un01 5.02 0.07 0.07 0.11 0.11
co03 ~~ ut04 5.02 0.09 0.09 0.10 0.10
ut03 ~~ un04 4.99 -0.08 -0.08 -0.11 -0.11
ut12 ~~ fa06 4.97 0.08 0.08 0.11 0.11
ut05 ~~ fa09 4.94 -0.10 -0.10 -0.11 -0.11
pr02 ~~ de01 4.94 -0.07 -0.07 -0.11 -0.11
ut04 ~~ de08 4.92 0.08 0.08 0.11 0.11
co10 ~~ fa09 4.90 -0.10 -0.10 -0.10 -0.10
ut01 ~~ fa05 4.89 0.05 0.05 0.13 0.13
pr05 ~~ fa04 4.88 0.10 0.10 0.10 0.10
pr08 ~~ de02 4.86 -0.06 -0.06 -0.11 -0.11
un07 ~~ un12 4.86 -0.07 -0.07 -0.11 -0.11
fa09 ~~ un11 4.86 -0.08 -0.08 -0.11 -0.11
fa06 ~~ un07 4.83 0.08 0.08 0.11 0.11
co04 ~~ ut02 4.82 0.07 0.07 0.11 0.11
co04 ~~ un09 4.80 -0.09 -0.09 -0.10 -0.10
co03 ~~ de09 4.80 0.10 0.10 0.10 0.10
pr05 ~~ co09 4.79 0.08 0.08 0.11 0.11
ut07 ~~ fa10 4.76 0.09 0.09 0.10 0.10
ut03 ~~ de10 4.74 -0.08 -0.08 -0.10 -0.10
fa06 ~~ un03 4.71 0.09 0.09 0.10 0.10
fa10 ~~ un10 4.70 -0.08 -0.08 -0.10 -0.10
fa04 ~~ un05 4.65 -0.07 -0.07 -0.10 -0.10
fa05 ~~ de10 4.63 0.06 0.06 0.12 0.12
fa04 ~~ de06 4.63 0.10 0.10 0.10 0.10
fa08 ~~ un04 4.62 -0.08 -0.08 -0.10 -0.10
co02 ~~ ut05 4.60 0.07 0.07 0.11 0.11
fa05 ~~ de03 4.58 0.07 0.07 0.12 0.12
ut02 ~~ ut08 4.58 -0.05 -0.05 -0.12 -0.12
ut04 ~~ fa08 4.57 0.10 0.10 0.10 0.10
co03 ~~ co06 4.55 -0.08 -0.08 -0.10 -0.10
pr02 ~~ co06 4.54 0.07 0.07 0.10 0.10
de01 ~~ de03 4.54 0.09 0.09 0.10 0.10
co09 ~~ un05 4.52 -0.05 -0.05 -0.11 -0.11
pr10 ~~ co02 4.51 0.07 0.07 0.10 0.10
de05 ~~ un03 4.50 0.10 0.10 0.10 0.10
fa10 ~~ de05 4.49 0.09 0.09 0.10 0.10
pr09 ~~ un01 4.47 0.06 0.06 0.10 0.10
co03 ~~ fa01 4.45 0.06 0.06 0.11 0.11
un03 ~~ un08 4.43 -0.08 -0.08 -0.10 -0.10
co05 ~~ un01 4.40 -0.05 -0.05 -0.11 -0.11
co03 ~~ de02 4.39 -0.07 -0.07 -0.10 -0.10
pr08 ~~ un01 4.37 0.05 0.05 0.10 0.10
pr08 ~~ co04 4.37 0.06 0.06 0.10 0.10
de02 ~~ un10 4.37 0.07 0.07 0.10 0.10
fa01 ~~ fa08 4.36 -0.07 -0.07 -0.12 -0.12
pr09 ~~ ut06 4.34 -0.06 -0.06 -0.10 -0.10
pr01 ~~ co09 4.34 -0.05 -0.05 -0.11 -0.11
co05 ~~ de01 4.31 0.07 0.07 0.11 0.11
ut07 ~~ de01 4.29 0.08 0.08 0.10 0.10
pr06 ~~ fa01 4.29 -0.06 -0.06 -0.11 -0.11
ut04 ~~ de07 4.28 0.08 0.08 0.10 0.10
de10 ~~ un10 4.28 0.07 0.07 0.10 0.10
de01 ~~ de05 4.27 -0.09 -0.09 -0.10 -0.10
ut02 ~~ de03 4.25 -0.06 -0.06 -0.11 -0.11
de01 ~~ un08 4.23 0.07 0.07 0.10 0.10
pr05 ~~ un06 4.19 0.10 0.10 0.10 0.10
co06 ~~ co09 4.19 0.07 0.07 0.11 0.11
pr08 ~~ ut11 4.18 -0.06 -0.06 -0.10 -0.10
de06 ~~ un07 4.16 0.08 0.08 0.10 0.10
pr10 ~~ fa02 4.15 -0.09 -0.09 -0.09 -0.09
un06 ~~ un08 4.14 -0.08 -0.08 -0.10 -0.10
de05 ~~ un02 4.12 -0.06 -0.06 -0.10 -0.10
ut06 ~~ un05 4.12 0.05 0.05 0.10 0.10
de02 ~~ de07 4.06 -0.07 -0.07 -0.10 -0.10
fa02 ~~ de06 4.05 0.10 0.10 0.09 0.09
co05 ~~ un07 4.04 0.06 0.06 0.10 0.10
ut06 ~~ de08 4.03 -0.06 -0.06 -0.10 -0.10
pr07 ~~ ut07 4.03 0.07 0.07 0.10 0.10
ut02 ~~ fa06 4.02 -0.05 -0.05 -0.10 -0.10
co10 ~~ un01 3.97 -0.06 -0.06 -0.10 -0.10
de02 ~~ de10 3.96 0.07 0.07 0.10 0.10
de01 ~~ de02 3.96 0.07 0.07 0.10 0.10
fa04 ~~ de07 3.95 -0.08 -0.08 -0.09 -0.09
pr02 ~~ co10 3.93 0.06 0.06 0.09 0.09
co06 ~~ fa08 3.93 -0.09 -0.09 -0.09 -0.09
de10 ~~ un03 3.90 -0.08 -0.08 -0.10 -0.10
ut09 ~~ fa05 3.90 -0.06 -0.06 -0.11 -0.11
de02 ~~ de09 3.90 -0.09 -0.09 -0.09 -0.09
pr06 ~~ co05 3.90 -0.06 -0.06 -0.10 -0.10
pr08 ~~ co08 3.88 -0.06 -0.06 -0.09 -0.09
pr07 ~~ de10 3.87 0.06 0.06 0.10 0.10
de06 ~~ un05 3.86 -0.06 -0.06 -0.10 -0.10
un04 ~~ un08 3.85 -0.06 -0.06 -0.10 -0.10
pr02 ~~ ut04 3.85 0.07 0.07 0.09 0.09
de02 ~~ un12 3.85 -0.06 -0.06 -0.10 -0.10
ut05 ~~ de01 3.82 0.08 0.08 0.09 0.09
co04 ~~ un04 3.81 -0.07 -0.07 -0.09 -0.09
fa09 ~~ un02 3.77 0.06 0.06 0.10 0.10
de09 ~~ un12 3.76 0.08 0.08 0.09 0.09
ut03 ~~ ut07 3.76 -0.08 -0.08 -0.09 -0.09
co03 ~~ co09 3.76 -0.06 -0.06 -0.10 -0.10
co02 ~~ ut02 3.75 -0.05 -0.05 -0.10 -0.10
de05 ~~ un01 3.74 0.06 0.06 0.09 0.09
fa09 ~~ un10 3.73 -0.08 -0.08 -0.09 -0.09
co06 ~~ fa02 3.73 -0.09 -0.09 -0.09 -0.09
pr01 ~~ un11 3.72 -0.05 -0.05 -0.10 -0.10
de07 ~~ un06 3.72 -0.08 -0.08 -0.09 -0.09
ut02 ~~ de09 3.71 0.06 0.06 0.10 0.10
de01 ~~ un02 3.71 0.05 0.05 0.10 0.10
ut05 ~~ un11 3.69 0.07 0.07 0.09 0.09
de03 ~~ de07 3.68 -0.07 -0.07 -0.09 -0.09
co09 ~~ de09 3.67 -0.08 -0.08 -0.09 -0.09
fa02 ~~ de10 3.66 0.08 0.08 0.09 0.09
ut02 ~~ de07 3.65 -0.05 -0.05 -0.10 -0.10
un03 ~~ un05 3.62 0.06 0.06 0.09 0.09
ut09 ~~ de03 3.60 -0.08 -0.08 -0.09 -0.09
co02 ~~ fa01 3.59 0.05 0.05 0.11 0.11
pr10 ~~ co10 3.59 0.07 0.07 0.09 0.09
de01 ~~ de08 3.59 0.06 0.06 0.09 0.09
ut04 ~~ un11 3.59 -0.07 -0.07 -0.09 -0.09
co04 ~~ ut05 3.55 -0.08 -0.08 -0.09 -0.09
un06 ~~ un10 3.53 0.08 0.08 0.09 0.09
ut07 ~~ ut11 3.53 -0.09 -0.09 -0.09 -0.09
fa02 ~~ un08 3.53 0.07 0.07 0.09 0.09
un06 ~~ un12 3.52 -0.07 -0.07 -0.09 -0.09
un01 ~~ un12 3.52 0.05 0.05 0.09 0.09
de05 ~~ de06 3.50 -0.08 -0.08 -0.09 -0.09
co06 ~~ un03 3.48 0.08 0.08 0.09 0.09
co06 ~~ fa01 3.47 -0.05 -0.05 -0.10 -0.10
pr01 ~~ ut12 3.45 0.05 0.05 0.09 0.09
ut08 ~~ de07 3.43 -0.06 -0.06 -0.09 -0.09
fa01 ~~ fa10 3.42 0.07 0.07 0.12 0.12
co01 ~~ de03 3.42 0.06 0.06 0.09 0.09
un02 ~~ un08 3.41 -0.05 -0.05 -0.10 -0.10
ut03 ~~ un09 3.41 -0.07 -0.07 -0.09 -0.09
pr05 ~~ fa05 3.41 -0.06 -0.06 -0.10 -0.10
pr02 ~~ fa04 3.41 0.07 0.07 0.09 0.09
fa06 ~~ un01 3.39 -0.05 -0.05 -0.09 -0.09
ut01 ~~ de08 3.38 -0.05 -0.05 -0.09 -0.09
pr05 ~~ un10 3.38 0.07 0.07 0.09 0.09
pr09 ~~ un08 3.36 0.05 0.05 0.09 0.09
ut01 ~~ fa08 3.36 -0.06 -0.06 -0.09 -0.09
ut01 ~~ ut04 3.36 0.06 0.06 0.09 0.09
co10 ~~ un08 3.36 0.06 0.06 0.09 0.09
fa02 ~~ de09 3.36 0.10 0.10 0.08 0.08
ut03 ~~ de08 3.35 0.06 0.06 0.09 0.09
pr08 ~~ pr10 3.34 0.05 0.05 0.09 0.09
un04 ~~ un06 3.33 0.07 0.07 0.09 0.09
co09 ~~ de01 3.31 -0.06 -0.06 -0.09 -0.09
pr09 ~~ de02 3.31 0.06 0.06 0.09 0.09
co01 ~~ ut08 3.27 0.05 0.05 0.09 0.09
pr07 ~~ un12 3.26 -0.05 -0.05 -0.09 -0.09
ut12 ~~ de07 3.25 -0.06 -0.06 -0.09 -0.09
co03 ~~ fa05 3.24 -0.05 -0.05 -0.10 -0.10
co06 ~~ fa06 3.23 0.07 0.07 0.09 0.09
fa05 ~~ un10 3.21 0.05 0.05 0.10 0.10
un07 ~~ un08 3.20 -0.06 -0.06 -0.09 -0.09
ut01 ~~ un12 3.19 0.05 0.05 0.09 0.09
fa04 ~~ fa05 3.18 0.06 0.06 0.10 0.10
ut01 ~~ de06 3.17 -0.06 -0.06 -0.09 -0.09
ut03 ~~ un05 3.17 0.05 0.05 0.09 0.09
pr09 ~~ co08 3.14 -0.06 -0.06 -0.08 -0.08
pr09 ~~ fa08 3.14 0.07 0.07 0.08 0.08
pr02 ~~ co02 3.13 0.05 0.05 0.09 0.09
pr05 ~~ pr09 3.12 -0.06 -0.06 -0.08 -0.08
ut03 ~~ ut08 3.11 -0.07 -0.07 -0.08 -0.08
pr07 ~~ un01 3.11 -0.05 -0.05 -0.09 -0.09
ut01 ~~ de01 3.10 -0.05 -0.05 -0.09 -0.09
ut06 ~~ fa02 3.08 -0.07 -0.07 -0.08 -0.08
de09 ~~ un11 3.05 -0.07 -0.07 -0.08 -0.08
ut09 ~~ de07 3.04 0.06 0.06 0.08 0.08
de02 ~~ un05 3.00 -0.05 -0.05 -0.09 -0.09
fa08 ~~ de05 3.00 -0.08 -0.08 -0.08 -0.08
pr06 ~~ de08 3.00 -0.06 -0.06 -0.08 -0.08
pr07 ~~ de05 2.99 -0.06 -0.06 -0.08 -0.08
ut07 ~~ un07 2.99 0.06 0.06 0.08 0.08
fa06 ~~ un09 2.98 0.06 0.06 0.08 0.08
fa05 ~~ un06 2.98 -0.06 -0.06 -0.09 -0.09
ut12 ~~ un09 2.97 -0.06 -0.06 -0.08 -0.08
ut04 ~~ fa04 2.97 -0.08 -0.08 -0.08 -0.08
ut08 ~~ de02 2.97 0.06 0.06 0.08 0.08
co02 ~~ un10 2.96 0.05 0.05 0.08 0.08
pr01 ~~ de05 2.96 0.05 0.05 0.09 0.09
ut04 ~~ un08 2.95 0.06 0.06 0.08 0.08
ut09 ~~ fa04 2.93 0.07 0.07 0.08 0.08
ut03 ~~ de03 2.93 -0.07 -0.07 -0.08 -0.08
co09 ~~ de10 2.92 0.05 0.05 0.09 0.09
de03 ~~ un11 2.92 -0.06 -0.06 -0.08 -0.08
ut08 ~~ un08 2.92 0.05 0.05 0.08 0.08
ut12 ~~ un03 2.92 -0.07 -0.07 -0.08 -0.08
ut05 ~~ ut08 2.91 -0.06 -0.06 -0.08 -0.08
pr05 ~~ co01 2.91 -0.06 -0.06 -0.08 -0.08
fa05 ~~ de05 2.91 -0.05 -0.05 -0.09 -0.09
de07 ~~ un09 2.91 0.06 0.06 0.08 0.08
pr09 ~~ ut05 2.88 -0.06 -0.06 -0.08 -0.08
ut07 ~~ un06 2.88 -0.08 -0.08 -0.08 -0.08
un04 ~~ un12 2.87 0.05 0.05 0.08 0.08
pr05 ~~ fa09 2.87 0.08 0.08 0.08 0.08
pr08 ~~ un08 2.86 0.04 0.04 0.08 0.08
ut08 ~~ fa06 2.83 0.06 0.06 0.08 0.08
fa10 ~~ un06 2.82 -0.08 -0.08 -0.08 -0.08
pr02 ~~ fa02 2.82 -0.07 -0.07 -0.08 -0.08
pr01 ~~ un09 2.82 -0.05 -0.05 -0.08 -0.08
ut04 ~~ ut05 2.82 -0.07 -0.07 -0.08 -0.08
co09 ~~ un01 2.81 -0.04 -0.04 -0.08 -0.08
ut06 ~~ un08 2.81 -0.05 -0.05 -0.08 -0.08
fa08 ~~ de06 2.81 0.08 0.08 0.08 0.08
de02 ~~ un11 2.81 0.05 0.05 0.08 0.08
ut12 ~~ un01 2.81 0.05 0.05 0.08 0.08
pr05 ~~ fa02 2.80 0.08 0.08 0.08 0.08
pr10 ~~ un09 2.80 -0.06 -0.06 -0.08 -0.08
pr01 ~~ co05 2.80 0.04 0.04 0.09 0.09
ut11 ~~ un01 2.79 -0.06 -0.06 -0.08 -0.08
ut07 ~~ un09 2.79 -0.07 -0.07 -0.08 -0.08
pr07 ~~ pr09 2.78 -0.05 -0.05 -0.08 -0.08
fa05 ~~ un05 2.77 0.04 0.04 0.10 0.10
co02 ~~ un09 2.77 -0.06 -0.06 -0.08 -0.08
de02 ~~ un09 2.76 0.06 0.06 0.08 0.08
fa01 ~~ de07 2.75 0.04 0.04 0.09 0.09
co10 ~~ ut01 2.75 0.05 0.05 0.08 0.08
fa10 ~~ un09 2.74 -0.07 -0.07 -0.08 -0.08
co10 ~~ de10 2.74 -0.06 -0.06 -0.08 -0.08
co02 ~~ un12 2.74 -0.05 -0.05 -0.08 -0.08
ut03 ~~ un12 2.74 0.06 0.06 0.08 0.08
un01 ~~ un11 2.73 -0.05 -0.05 -0.08 -0.08
pr07 ~~ co08 2.70 0.06 0.06 0.08 0.08
co05 ~~ co10 2.70 -0.05 -0.05 -0.09 -0.09
pr07 ~~ co09 2.69 0.05 0.05 0.08 0.08
co10 ~~ fa02 2.68 -0.07 -0.07 -0.08 -0.08
ut11 ~~ fa10 2.68 -0.08 -0.08 -0.08 -0.08
un09 ~~ un11 2.68 0.06 0.06 0.08 0.08
pr02 ~~ co05 2.68 -0.05 -0.05 -0.08 -0.08
fa05 ~~ de01 2.67 0.05 0.05 0.09 0.09
un02 ~~ un07 2.67 0.05 0.05 0.08 0.08
co06 ~~ ut12 2.67 -0.06 -0.06 -0.08 -0.08
ut02 ~~ un01 2.67 0.03 0.03 0.09 0.09
fa09 ~~ de06 2.67 0.08 0.08 0.08 0.08
de02 ~~ de03 2.66 0.06 0.06 0.08 0.08
de08 ~~ un01 2.66 0.04 0.04 0.08 0.08
co03 ~~ un06 2.66 -0.07 -0.07 -0.08 -0.08
co03 ~~ ut09 2.66 0.06 0.06 0.08 0.08
fa02 ~~ un09 2.65 -0.07 -0.07 -0.08 -0.08
co09 ~~ de07 2.63 -0.05 -0.05 -0.08 -0.08
ut02 ~~ fa02 2.63 0.05 0.05 0.08 0.08
co03 ~~ un12 2.62 0.05 0.05 0.08 0.08
de10 ~~ un07 2.59 0.06 0.06 0.08 0.08
ut08 ~~ fa05 2.59 -0.04 -0.04 -0.09 -0.09
ut01 ~~ de10 2.59 -0.05 -0.05 -0.08 -0.08
co03 ~~ co10 2.58 -0.06 -0.06 -0.08 -0.08
pr07 ~~ ut02 2.58 -0.04 -0.04 -0.09 -0.09
pr02 ~~ pr08 2.57 -0.04 -0.04 -0.08 -0.08
co05 ~~ ut06 2.57 0.04 0.04 0.08 0.08
co09 ~~ co10 2.56 -0.05 -0.05 -0.08 -0.08
pr01 ~~ fa08 2.55 -0.05 -0.05 -0.08 -0.08
un11 ~~ un12 2.55 0.05 0.05 0.08 0.08
co08 ~~ un12 2.54 -0.06 -0.06 -0.08 -0.08
pr06 ~~ de10 2.53 -0.06 -0.06 -0.08 -0.08
co09 ~~ un08 2.53 -0.04 -0.04 -0.08 -0.08
un08 ~~ un12 2.52 0.05 0.05 0.08 0.08
ut02 ~~ de01 2.51 -0.04 -0.04 -0.08 -0.08
fa04 ~~ un02 2.50 0.05 0.05 0.08 0.08
pr02 ~~ ut09 2.50 -0.05 -0.05 -0.08 -0.08
fa09 ~~ de08 2.49 -0.06 -0.06 -0.08 -0.08
ut02 ~~ ut06 2.48 -0.04 -0.04 -0.09 -0.09
pr07 ~~ co04 2.48 -0.06 -0.06 -0.08 -0.08
co05 ~~ un08 2.48 -0.04 -0.04 -0.08 -0.08
ut12 ~~ de06 2.47 -0.06 -0.06 -0.08 -0.08
co10 ~~ ut06 2.46 -0.05 -0.05 -0.08 -0.08
ut06 ~~ ut07 2.46 0.05 0.05 0.08 0.08
co02 ~~ un07 2.45 0.05 0.05 0.08 0.08
co09 ~~ ut12 2.45 -0.05 -0.05 -0.08 -0.08
co04 ~~ un03 2.45 0.07 0.07 0.07 0.07
fa01 ~~ de01 2.44 -0.05 -0.05 -0.08 -0.08
co03 ~~ ut12 2.43 -0.05 -0.05 -0.07 -0.07
pr10 ~~ ut07 2.43 0.06 0.06 0.07 0.07
co05 ~~ de10 2.42 0.05 0.05 0.08 0.08
ut02 ~~ de02 2.42 -0.04 -0.04 -0.08 -0.08
fa09 ~~ un01 2.42 -0.05 -0.05 -0.07 -0.07
pr06 ~~ fa05 2.41 0.04 0.04 0.09 0.09
fa10 ~~ un05 2.41 0.05 0.05 0.08 0.08
un03 ~~ un11 2.41 -0.06 -0.06 -0.08 -0.08
fa05 ~~ un08 2.40 -0.04 -0.04 -0.09 -0.09
co08 ~~ ut07 2.40 0.07 0.07 0.07 0.07
un01 ~~ un09 2.39 -0.05 -0.05 -0.08 -0.08
fa01 ~~ de10 2.38 -0.04 -0.04 -0.09 -0.09
fa02 ~~ de05 2.38 -0.08 -0.08 -0.07 -0.07
co01 ~~ ut11 2.38 -0.06 -0.06 -0.08 -0.08
pr10 ~~ fa06 2.38 0.06 0.06 0.07 0.07
co02 ~~ co06 2.38 0.05 0.05 0.08 0.08
fa08 ~~ un08 2.37 0.06 0.06 0.07 0.07
ut02 ~~ un06 2.36 0.05 0.05 0.08 0.08
de09 ~~ un03 2.36 0.08 0.08 0.07 0.07
un02 ~~ un11 2.35 -0.04 -0.04 -0.08 -0.08
fa02 ~~ de02 2.33 -0.06 -0.06 -0.07 -0.07
pr10 ~~ ut03 2.33 -0.06 -0.06 -0.07 -0.07
co01 ~~ de06 2.32 -0.05 -0.05 -0.08 -0.08
co01 ~~ un11 2.32 0.04 0.04 0.08 0.08
pr06 ~~ un03 2.29 0.06 0.06 0.07 0.07
pr09 ~~ un09 2.28 -0.05 -0.05 -0.07 -0.07
co10 ~~ de08 2.27 -0.05 -0.05 -0.07 -0.07
co08 ~~ de02 2.26 0.06 0.06 0.07 0.07
co04 ~~ un02 2.26 -0.05 -0.05 -0.07 -0.07
ut08 ~~ un12 2.26 -0.05 -0.05 -0.07 -0.07
un05 ~~ un08 2.25 0.04 0.04 0.08 0.08
fa01 ~~ un08 2.25 0.04 0.04 0.08 0.08
ut01 ~~ ut07 2.24 -0.05 -0.05 -0.08 -0.08
ut06 ~~ ut08 2.23 0.04 0.04 0.07 0.07
co09 ~~ un11 2.22 0.04 0.04 0.08 0.08
pr01 ~~ de01 2.21 0.04 0.04 0.07 0.07
pr10 ~~ ut01 2.21 0.04 0.04 0.07 0.07
pr06 ~~ un09 2.21 0.05 0.05 0.07 0.07
pr05 ~~ ut05 2.20 0.06 0.06 0.07 0.07
pr10 ~~ un10 2.20 -0.05 -0.05 -0.07 -0.07
ut07 ~~ de02 2.19 0.05 0.05 0.07 0.07
fa10 ~~ un03 2.19 -0.07 -0.07 -0.07 -0.07
de06 ~~ de09 2.18 -0.08 -0.08 -0.07 -0.07
pr01 ~~ de02 2.18 -0.04 -0.04 -0.08 -0.08
ut12 ~~ fa02 2.18 -0.06 -0.06 -0.07 -0.07
ut07 ~~ de08 2.16 0.05 0.05 0.07 0.07
pr09 ~~ un04 2.15 -0.04 -0.04 -0.07 -0.07
pr07 ~~ un03 2.15 -0.05 -0.05 -0.07 -0.07
pr01 ~~ un10 2.14 -0.04 -0.04 -0.07 -0.07
pr05 ~~ fa10 2.14 -0.07 -0.07 -0.07 -0.07
co01 ~~ fa04 2.12 0.05 0.05 0.07 0.07
un03 ~~ un07 2.12 -0.06 -0.06 -0.07 -0.07
fa01 ~~ un06 2.12 -0.05 -0.05 -0.08 -0.08
pr09 ~~ ut02 2.11 -0.03 -0.03 -0.07 -0.07
co06 ~~ un08 2.10 -0.05 -0.05 -0.07 -0.07
pr01 ~~ ut05 2.10 -0.04 -0.04 -0.07 -0.07
co05 ~~ un10 2.10 0.04 0.04 0.07 0.07
pr06 ~~ fa02 2.09 -0.06 -0.06 -0.07 -0.07
ut05 ~~ un01 2.07 -0.04 -0.04 -0.07 -0.07
pr06 ~~ un12 2.07 0.05 0.05 0.07 0.07
ut12 ~~ fa10 2.07 -0.06 -0.06 -0.07 -0.07
ut01 ~~ ut12 2.07 -0.04 -0.04 -0.07 -0.07
fa02 ~~ de08 2.06 -0.06 -0.06 -0.07 -0.07
ut07 ~~ fa02 2.04 -0.07 -0.07 -0.07 -0.07
pr08 ~~ fa05 2.04 -0.03 -0.03 -0.08 -0.08
ut02 ~~ ut11 2.03 -0.05 -0.05 -0.07 -0.07
de08 ~~ un07 2.03 0.05 0.05 0.07 0.07
ut04 ~~ de10 2.03 0.06 0.06 0.07 0.07
fa08 ~~ de07 2.03 -0.06 -0.06 -0.07 -0.07
ut08 ~~ fa02 2.02 -0.06 -0.06 -0.07 -0.07
pr05 ~~ co03 2.01 0.06 0.06 0.07 0.07
pr06 ~~ ut01 2.01 0.04 0.04 0.07 0.07
fa05 ~~ fa06 2.01 0.05 0.05 0.09 0.09
co03 ~~ un05 2.00 0.04 0.04 0.07 0.07
pr01 ~~ de07 2.00 -0.03 -0.03 -0.07 -0.07
pr02 ~~ un08 2.00 0.04 0.04 0.07 0.07
co02 ~~ ut04 2.00 0.05 0.05 0.07 0.07
pr07 ~~ ut06 2.00 -0.04 -0.04 -0.07 -0.07
pr08 ~~ fa09 1.99 -0.04 -0.04 -0.07 -0.07
pr07 ~~ co02 1.99 -0.04 -0.04 -0.07 -0.07
ut04 ~~ de02 1.98 -0.05 -0.05 -0.07 -0.07
pr01 ~~ un07 1.98 -0.04 -0.04 -0.07 -0.07
pr08 ~~ fa04 1.98 -0.04 -0.04 -0.07 -0.07
ut08 ~~ de03 1.98 0.05 0.05 0.07 0.07
co05 ~~ ut02 1.97 -0.03 -0.03 -0.08 -0.08
un01 ~~ un03 1.96 -0.05 -0.05 -0.07 -0.07
de06 ~~ un02 1.96 0.04 0.04 0.07 0.07
co06 ~~ de09 1.95 0.07 0.07 0.07 0.07
un01 ~~ un06 1.94 -0.05 -0.05 -0.07 -0.07
ut03 ~~ de02 1.94 -0.05 -0.05 -0.07 -0.07
de09 ~~ un08 1.93 0.06 0.06 0.07 0.07
de02 ~~ un02 1.93 0.04 0.04 0.07 0.07
ut09 ~~ un05 1.93 -0.04 -0.04 -0.07 -0.07
ut05 ~~ de09 1.92 -0.07 -0.07 -0.06 -0.06
ut05 ~~ ut11 1.92 -0.06 -0.06 -0.07 -0.07
de01 ~~ un05 1.92 -0.04 -0.04 -0.07 -0.07
pr01 ~~ un02 1.91 -0.03 -0.03 -0.07 -0.07
ut07 ~~ fa01 1.91 0.04 0.04 0.07 0.07
pr08 ~~ fa10 1.91 0.04 0.04 0.07 0.07
ut05 ~~ un05 1.90 -0.04 -0.04 -0.07 -0.07
ut01 ~~ ut08 1.90 -0.04 -0.04 -0.07 -0.07
co05 ~~ ut11 1.90 0.05 0.05 0.07 0.07
pr01 ~~ un12 1.89 0.03 0.03 0.07 0.07
co08 ~~ fa02 1.89 -0.07 -0.07 -0.06 -0.06
de07 ~~ un03 1.88 0.05 0.05 0.06 0.06
pr02 ~~ un02 1.87 0.03 0.03 0.07 0.07
fa09 ~~ un08 1.86 0.05 0.05 0.07 0.07
fa04 ~~ un04 1.86 0.05 0.05 0.06 0.06
fa08 ~~ un11 1.85 -0.05 -0.05 -0.06 -0.06
ut02 ~~ de10 1.84 -0.03 -0.03 -0.07 -0.07
pr10 ~~ fa01 1.82 -0.04 -0.04 -0.07 -0.07
ut02 ~~ fa09 1.82 0.04 0.04 0.07 0.07
pr08 ~~ pr09 1.80 0.03 0.03 0.07 0.07
co05 ~~ un02 1.80 0.03 0.03 0.07 0.07
de03 ~~ un09 1.79 -0.05 -0.05 -0.06 -0.06
co05 ~~ de07 1.79 -0.04 -0.04 -0.07 -0.07
co10 ~~ fa04 1.77 0.06 0.06 0.06 0.06
ut06 ~~ un12 1.76 -0.04 -0.04 -0.07 -0.07
fa08 ~~ de01 1.76 -0.06 -0.06 -0.06 -0.06
pr10 ~~ un11 1.76 0.04 0.04 0.06 0.06
co05 ~~ de09 1.75 -0.05 -0.05 -0.07 -0.07
co02 ~~ co09 1.75 -0.04 -0.04 -0.07 -0.07
fa02 ~~ de07 1.75 -0.05 -0.05 -0.06 -0.06
ut04 ~~ un09 1.75 0.05 0.05 0.06 0.06
pr06 ~~ co02 1.75 -0.04 -0.04 -0.06 -0.06
ut05 ~~ de05 1.74 -0.06 -0.06 -0.06 -0.06
co03 ~~ un04 1.74 -0.04 -0.04 -0.06 -0.06
ut01 ~~ de07 1.74 0.03 0.03 0.07 0.07
co02 ~~ de02 1.73 0.04 0.04 0.07 0.07
pr05 ~~ fa06 1.72 0.05 0.05 0.06 0.06
ut04 ~~ fa05 1.72 0.04 0.04 0.07 0.07
de10 ~~ un11 1.72 0.04 0.04 0.07 0.07
de02 ~~ un08 1.72 -0.04 -0.04 -0.06 -0.06
co10 ~~ de03 1.72 0.05 0.05 0.06 0.06
pr10 ~~ co05 1.71 -0.04 -0.04 -0.07 -0.07
co01 ~~ ut01 1.71 -0.03 -0.03 -0.07 -0.07
pr09 ~~ co09 1.71 -0.04 -0.04 -0.06 -0.06
co10 ~~ ut02 1.69 0.03 0.03 0.07 0.07
de08 ~~ un12 1.68 0.04 0.04 0.06 0.06
pr05 ~~ un11 1.68 0.05 0.05 0.06 0.06
pr01 ~~ de06 1.68 -0.04 -0.04 -0.07 -0.07
co01 ~~ de09 1.68 -0.05 -0.05 -0.06 -0.06
ut09 ~~ un03 1.68 -0.05 -0.05 -0.06 -0.06
ut07 ~~ de09 1.67 -0.06 -0.06 -0.06 -0.06
pr08 ~~ ut04 1.66 -0.04 -0.04 -0.06 -0.06
pr08 ~~ co10 1.65 -0.03 -0.03 -0.06 -0.06
pr05 ~~ un02 1.65 -0.04 -0.04 -0.06 -0.06
ut01 ~~ de09 1.65 0.05 0.05 0.06 0.06
ut09 ~~ de06 1.64 -0.05 -0.05 -0.06 -0.06
co04 ~~ un11 1.63 -0.05 -0.05 -0.06 -0.06
co06 ~~ de07 1.63 0.04 0.04 0.06 0.06
fa08 ~~ un07 1.63 -0.05 -0.05 -0.06 -0.06
ut04 ~~ un02 1.62 -0.04 -0.04 -0.06 -0.06
pr10 ~~ un02 1.62 0.04 0.04 0.06 0.06
de06 ~~ un04 1.62 0.04 0.04 0.06 0.06
pr09 ~~ ut09 1.61 -0.04 -0.04 -0.06 -0.06
pr08 ~~ un11 1.61 0.03 0.03 0.06 0.06
ut12 ~~ de10 1.61 0.04 0.04 0.06 0.06
co02 ~~ de10 1.61 -0.04 -0.04 -0.06 -0.06
ut12 ~~ fa01 1.61 -0.04 -0.04 -0.07 -0.07
co05 ~~ un09 1.61 -0.04 -0.04 -0.07 -0.07
pr07 ~~ pr08 1.60 0.03 0.03 0.07 0.07
ut03 ~~ de06 1.59 -0.05 -0.05 -0.06 -0.06
ut09 ~~ un06 1.59 -0.06 -0.06 -0.06 -0.06
de06 ~~ de07 1.59 -0.05 -0.05 -0.06 -0.06
ut08 ~~ un07 1.58 -0.04 -0.04 -0.06 -0.06
co06 ~~ un11 1.57 -0.04 -0.04 -0.06 -0.06
co01 ~~ fa06 1.57 0.04 0.04 0.06 0.06
fa06 ~~ un06 1.57 -0.05 -0.05 -0.06 -0.06
ut03 ~~ un03 1.57 0.05 0.05 0.06 0.06
pr07 ~~ fa04 1.57 0.05 0.05 0.06 0.06
ut06 ~~ un06 1.56 -0.05 -0.05 -0.06 -0.06
pr09 ~~ de07 1.56 -0.04 -0.04 -0.06 -0.06
un04 ~~ un11 1.53 -0.04 -0.04 -0.06 -0.06
fa04 ~~ de01 1.53 0.05 0.05 0.06 0.06
co08 ~~ de07 1.53 -0.04 -0.04 -0.06 -0.06
de09 ~~ un02 1.52 -0.04 -0.04 -0.06 -0.06
de10 ~~ un02 1.52 -0.03 -0.03 -0.06 -0.06
ut04 ~~ un12 1.51 0.04 0.04 0.06 0.06
ut06 ~~ un03 1.51 0.04 0.04 0.06 0.06
ut04 ~~ ut08 1.50 -0.05 -0.05 -0.06 -0.06
co09 ~~ fa01 1.50 -0.03 -0.03 -0.07 -0.07
ut06 ~~ de05 1.50 0.04 0.04 0.06 0.06
pr02 ~~ de03 1.50 0.04 0.04 0.06 0.06
pr05 ~~ de09 1.49 -0.06 -0.06 -0.06 -0.06
ut06 ~~ fa08 1.49 -0.04 -0.04 -0.06 -0.06
ut05 ~~ de03 1.49 -0.05 -0.05 -0.06 -0.06
pr09 ~~ de05 1.48 0.04 0.04 0.06 0.06
ut03 ~~ un11 1.48 -0.04 -0.04 -0.06 -0.06
pr01 ~~ co02 1.46 0.03 0.03 0.06 0.06
fa06 ~~ de10 1.46 0.04 0.04 0.06 0.06
pr02 ~~ un09 1.46 -0.04 -0.04 -0.06 -0.06
pr08 ~~ un06 1.45 -0.04 -0.04 -0.06 -0.06
ut01 ~~ ut06 1.45 -0.03 -0.03 -0.06 -0.06
fa05 ~~ un04 1.44 -0.03 -0.03 -0.07 -0.07
pr10 ~~ de05 1.44 0.05 0.05 0.06 0.06
co09 ~~ un09 1.44 0.04 0.04 0.06 0.06
un03 ~~ un12 1.43 -0.04 -0.04 -0.06 -0.06
co05 ~~ un03 1.41 -0.04 -0.04 -0.06 -0.06
fa02 ~~ un04 1.40 -0.04 -0.04 -0.06 -0.06
ut03 ~~ ut11 1.39 -0.06 -0.06 -0.05 -0.05
fa06 ~~ un02 1.39 0.03 0.03 0.06 0.06
fa09 ~~ un12 1.39 0.04 0.04 0.06 0.06
co10 ~~ ut08 1.39 -0.04 -0.04 -0.06 -0.06
pr07 ~~ co03 1.39 0.04 0.04 0.06 0.06
ut11 ~~ un07 1.39 0.05 0.05 0.06 0.06
co01 ~~ de07 1.38 -0.03 -0.03 -0.06 -0.06
ut06 ~~ de01 1.38 -0.04 -0.04 -0.06 -0.06
pr06 ~~ de09 1.38 -0.05 -0.05 -0.05 -0.05
un09 ~~ un10 1.37 0.04 0.04 0.06 0.06
ut03 ~~ ut09 1.37 -0.05 -0.05 -0.06 -0.06
pr10 ~~ fa08 1.37 -0.05 -0.05 -0.05 -0.05
co01 ~~ un02 1.37 -0.03 -0.03 -0.06 -0.06
co06 ~~ ut01 1.37 0.03 0.03 0.06 0.06
ut03 ~~ un01 1.36 0.04 0.04 0.06 0.06
co05 ~~ fa09 1.36 0.04 0.04 0.06 0.06
fa10 ~~ un12 1.35 0.04 0.04 0.06 0.06
ut06 ~~ ut09 1.34 0.04 0.04 0.06 0.06
de06 ~~ un03 1.34 -0.05 -0.05 -0.05 -0.05
de03 ~~ un07 1.34 0.04 0.04 0.06 0.06
ut04 ~~ un04 1.34 0.04 0.04 0.05 0.05
fa01 ~~ un11 1.33 0.03 0.03 0.06 0.06
co05 ~~ ut04 1.33 -0.04 -0.04 -0.06 -0.06
co09 ~~ de08 1.33 -0.03 -0.03 -0.06 -0.06
un07 ~~ un11 1.33 0.04 0.04 0.06 0.06
ut04 ~~ fa06 1.32 -0.05 -0.05 -0.05 -0.05
ut07 ~~ de10 1.32 -0.04 -0.04 -0.06 -0.06
de02 ~~ un04 1.31 -0.03 -0.03 -0.06 -0.06
co02 ~~ co08 1.31 -0.04 -0.04 -0.06 -0.06
un05 ~~ un06 1.30 -0.04 -0.04 -0.06 -0.06
co04 ~~ de01 1.30 -0.05 -0.05 -0.05 -0.05
pr08 ~~ ut03 1.30 0.03 0.03 0.05 0.05
co03 ~~ un02 1.29 -0.03 -0.03 -0.06 -0.06
ut08 ~~ fa04 1.28 0.04 0.04 0.05 0.05
pr08 ~~ un12 1.28 0.03 0.03 0.06 0.06
de09 ~~ un04 1.27 -0.04 -0.04 -0.05 -0.05
ut02 ~~ un11 1.27 -0.03 -0.03 -0.06 -0.06
ut05 ~~ un04 1.27 -0.04 -0.04 -0.05 -0.05
pr07 ~~ de01 1.25 -0.04 -0.04 -0.05 -0.05
co09 ~~ ut05 1.25 0.04 0.04 0.06 0.06
pr07 ~~ un11 1.25 0.03 0.03 0.06 0.06
fa09 ~~ un04 1.24 0.04 0.04 0.05 0.05
co02 ~~ co05 1.23 -0.03 -0.03 -0.06 -0.06
co02 ~~ co10 1.23 0.04 0.04 0.06 0.06
ut04 ~~ de03 1.23 0.05 0.05 0.05 0.05
pr07 ~~ ut08 1.23 0.03 0.03 0.05 0.05
ut11 ~~ un12 1.22 -0.04 -0.04 -0.05 -0.05
pr09 ~~ co10 1.22 -0.04 -0.04 -0.05 -0.05
de07 ~~ un01 1.22 0.03 0.03 0.05 0.05
de07 ~~ un12 1.22 0.03 0.03 0.05 0.05
co03 ~~ fa06 1.22 0.04 0.04 0.05 0.05
co01 ~~ ut07 1.21 -0.04 -0.04 -0.05 -0.05
ut09 ~~ fa08 1.21 -0.05 -0.05 -0.05 -0.05
un03 ~~ un10 1.21 0.04 0.04 0.05 0.05
fa09 ~~ de03 1.21 -0.05 -0.05 -0.05 -0.05
co04 ~~ de06 1.20 -0.05 -0.05 -0.05 -0.05
co04 ~~ de03 1.20 -0.05 -0.05 -0.05 -0.05
ut05 ~~ fa06 1.20 0.04 0.04 0.05 0.05
co04 ~~ ut11 1.20 -0.05 -0.05 -0.05 -0.05
pr01 ~~ co10 1.20 0.03 0.03 0.06 0.06
pr07 ~~ un10 1.20 0.03 0.03 0.05 0.05
ut07 ~~ fa05 1.19 -0.03 -0.03 -0.06 -0.06
ut02 ~~ fa01 1.18 0.02 0.02 0.06 0.06
co04 ~~ fa09 1.18 -0.05 -0.05 -0.05 -0.05
co06 ~~ ut08 1.18 0.04 0.04 0.05 0.05
ut01 ~~ fa09 1.17 -0.04 -0.04 -0.05 -0.05
fa05 ~~ fa10 1.15 -0.04 -0.04 -0.07 -0.07
co01 ~~ un09 1.15 0.03 0.03 0.05 0.05
co02 ~~ de08 1.14 -0.03 -0.03 -0.05 -0.05
co03 ~~ de10 1.14 -0.04 -0.04 -0.05 -0.05
co02 ~~ fa06 1.14 0.03 0.03 0.05 0.05
pr06 ~~ ut08 1.14 -0.04 -0.04 -0.05 -0.05
pr08 ~~ co09 1.14 -0.03 -0.03 -0.05 -0.05
fa08 ~~ un02 1.14 -0.03 -0.03 -0.05 -0.05
pr02 ~~ un07 1.14 -0.03 -0.03 -0.05 -0.05
fa05 ~~ un03 1.14 0.03 0.03 0.06 0.06
pr05 ~~ pr06 1.13 -0.04 -0.04 -0.05 -0.05
fa10 ~~ de09 1.13 0.05 0.05 0.05 0.05
co02 ~~ ut03 1.12 -0.04 -0.04 -0.05 -0.05
co05 ~~ un12 1.12 -0.03 -0.03 -0.05 -0.05
ut07 ~~ un03 1.12 -0.04 -0.04 -0.05 -0.05
pr02 ~~ pr07 1.11 -0.03 -0.03 -0.05 -0.05
pr06 ~~ de06 1.11 -0.04 -0.04 -0.05 -0.05
ut08 ~~ un09 1.10 -0.04 -0.04 -0.05 -0.05
ut09 ~~ un02 1.10 -0.03 -0.03 -0.05 -0.05
de07 ~~ de09 1.09 -0.04 -0.04 -0.05 -0.05
ut11 ~~ un04 1.09 0.04 0.04 0.05 0.05
ut09 ~~ fa02 1.09 0.05 0.05 0.05 0.05
ut05 ~~ de08 1.09 -0.04 -0.04 -0.05 -0.05
co01 ~~ un08 1.09 -0.03 -0.03 -0.05 -0.05
co09 ~~ fa02 1.09 0.04 0.04 0.05 0.05
pr05 ~~ un03 1.08 -0.05 -0.05 -0.05 -0.05
ut07 ~~ un04 1.07 0.03 0.03 0.05 0.05
pr07 ~~ un02 1.07 0.03 0.03 0.05 0.05
ut08 ~~ de06 1.07 -0.04 -0.04 -0.05 -0.05
pr06 ~~ un05 1.07 0.03 0.03 0.05 0.05
pr09 ~~ fa01 1.07 0.03 0.03 0.06 0.06
pr10 ~~ de10 1.06 -0.04 -0.04 -0.05 -0.05
fa08 ~~ un09 1.06 -0.05 -0.05 -0.05 -0.05
pr07 ~~ un04 1.05 -0.03 -0.03 -0.05 -0.05
fa01 ~~ de03 1.05 -0.03 -0.03 -0.06 -0.06
pr07 ~~ fa02 1.04 -0.04 -0.04 -0.05 -0.05
pr01 ~~ co03 1.04 -0.03 -0.03 -0.05 -0.05
co01 ~~ co05 1.04 0.03 0.03 0.06 0.06
co10 ~~ un02 1.04 0.03 0.03 0.05 0.05
pr02 ~~ fa10 1.04 -0.04 -0.04 -0.05 -0.05
fa09 ~~ un09 1.03 -0.04 -0.04 -0.05 -0.05
co10 ~~ un12 1.02 0.03 0.03 0.05 0.05
ut05 ~~ fa10 1.02 0.04 0.04 0.05 0.05
pr08 ~~ un09 1.01 -0.03 -0.03 -0.05 -0.05
fa02 ~~ un01 1.00 -0.04 -0.04 -0.05 -0.05
pr09 ~~ un10 1.00 -0.03 -0.03 -0.05 -0.05
co01 ~~ un12 1.00 -0.03 -0.03 -0.05 -0.05
co05 ~~ co06 0.97 0.03 0.03 0.05 0.05
co04 ~~ fa01 0.97 0.03 0.03 0.05 0.05
pr06 ~~ co10 0.97 0.04 0.04 0.05 0.05
ut09 ~~ un04 0.96 -0.03 -0.03 -0.05 -0.05
pr06 ~~ un04 0.96 0.03 0.03 0.05 0.05
co04 ~~ co10 0.96 0.04 0.04 0.05 0.05
co01 ~~ un04 0.96 -0.03 -0.03 -0.05 -0.05
co08 ~~ fa01 0.96 -0.03 -0.03 -0.05 -0.05
pr10 ~~ un03 0.95 0.04 0.04 0.05 0.05
ut08 ~~ un02 0.94 0.03 0.03 0.05 0.05
un05 ~~ un11 0.94 -0.03 -0.03 -0.05 -0.05
pr02 ~~ un10 0.94 0.03 0.03 0.05 0.05
pr07 ~~ de03 0.94 0.03 0.03 0.05 0.05
ut09 ~~ ut12 0.93 0.03 0.03 0.05 0.05
co06 ~~ un05 0.93 -0.03 -0.03 -0.05 -0.05
pr01 ~~ de09 0.93 0.03 0.03 0.05 0.05
de08 ~~ un11 0.92 0.03 0.03 0.05 0.05
ut11 ~~ un05 0.91 -0.03 -0.03 -0.05 -0.05
de01 ~~ un07 0.91 0.04 0.04 0.05 0.05
co01 ~~ fa09 0.91 -0.04 -0.04 -0.05 -0.05
ut12 ~~ un04 0.91 0.03 0.03 0.05 0.05
co01 ~~ fa02 0.91 0.04 0.04 0.05 0.05
ut06 ~~ un07 0.90 0.03 0.03 0.05 0.05
de08 ~~ de10 0.89 0.03 0.03 0.05 0.05
de08 ~~ un09 0.89 0.03 0.03 0.05 0.05
co02 ~~ co04 0.88 0.04 0.04 0.05 0.05
ut09 ~~ de08 0.87 0.03 0.03 0.05 0.05
ut06 ~~ fa04 0.87 0.03 0.03 0.04 0.04
co03 ~~ de01 0.86 -0.03 -0.03 -0.04 -0.04
pr02 ~~ fa05 0.86 -0.02 -0.02 -0.05 -0.05
pr10 ~~ un12 0.86 -0.03 -0.03 -0.04 -0.04
pr10 ~~ fa05 0.86 -0.03 -0.03 -0.05 -0.05
co10 ~~ fa06 0.86 0.03 0.03 0.04 0.04
ut05 ~~ ut12 0.85 0.03 0.03 0.05 0.05
pr10 ~~ ut09 0.85 -0.03 -0.03 -0.04 -0.04
pr10 ~~ ut06 0.85 0.03 0.03 0.04 0.04
pr10 ~~ de01 0.85 0.04 0.04 0.04 0.04
ut02 ~~ ut07 0.85 -0.03 -0.03 -0.05 -0.05
ut11 ~~ un08 0.85 -0.04 -0.04 -0.04 -0.04
de05 ~~ de07 0.84 0.03 0.03 0.04 0.04
co02 ~~ un02 0.84 0.02 0.02 0.05 0.05
ut08 ~~ un01 0.83 0.03 0.03 0.04 0.04
co09 ~~ ut06 0.83 -0.02 -0.02 -0.05 -0.05
co01 ~~ un03 0.83 -0.03 -0.03 -0.04 -0.04
ut12 ~~ fa09 0.83 -0.04 -0.04 -0.04 -0.04
pr05 ~~ un08 0.83 -0.03 -0.03 -0.04 -0.04
de07 ~~ un11 0.82 -0.03 -0.03 -0.04 -0.04
pr10 ~~ un08 0.82 0.03 0.03 0.04 0.04
ut01 ~~ un10 0.82 -0.02 -0.02 -0.05 -0.05
fa05 ~~ de02 0.82 0.02 0.02 0.05 0.05
de10 ~~ un04 0.81 0.03 0.03 0.04 0.04
co10 ~~ de07 0.81 -0.03 -0.03 -0.04 -0.04
de07 ~~ un02 0.81 -0.02 -0.02 -0.05 -0.05
un06 ~~ un07 0.81 0.04 0.04 0.04 0.04
pr08 ~~ ut05 0.81 -0.03 -0.03 -0.04 -0.04
co02 ~~ fa05 0.81 -0.02 -0.02 -0.05 -0.05
ut11 ~~ fa02 0.80 -0.05 -0.05 -0.04 -0.04
ut07 ~~ de03 0.80 -0.04 -0.04 -0.04 -0.04
un04 ~~ un10 0.80 -0.03 -0.03 -0.04 -0.04
co08 ~~ de08 0.80 -0.03 -0.03 -0.04 -0.04
co04 ~~ ut08 0.79 0.03 0.03 0.04 0.04
pr09 ~~ fa02 0.79 -0.03 -0.03 -0.04 -0.04
pr01 ~~ fa02 0.79 -0.03 -0.03 -0.04 -0.04
fa01 ~~ de05 0.79 0.03 0.03 0.05 0.05
de10 ~~ un12 0.79 -0.03 -0.03 -0.04 -0.04
pr01 ~~ ut09 0.79 0.02 0.02 0.04 0.04
co06 ~~ de10 0.78 -0.03 -0.03 -0.04 -0.04
pr08 ~~ fa01 0.78 0.02 0.02 0.05 0.05
ut04 ~~ de09 0.77 -0.04 -0.04 -0.04 -0.04
de01 ~~ un04 0.77 -0.03 -0.03 -0.04 -0.04
pr06 ~~ un01 0.76 -0.03 -0.03 -0.04 -0.04
fa04 ~~ un11 0.76 0.03 0.03 0.04 0.04
de07 ~~ de08 0.75 0.03 0.03 0.04 0.04
pr01 ~~ fa01 0.75 0.02 0.02 0.05 0.05
co02 ~~ ut06 0.75 -0.02 -0.02 -0.04 -0.04
de03 ~~ un04 0.75 -0.03 -0.03 -0.04 -0.04
pr10 ~~ co04 0.74 0.04 0.04 0.04 0.04
ut09 ~~ un10 0.74 0.03 0.03 0.04 0.04
ut08 ~~ un10 0.73 -0.03 -0.03 -0.04 -0.04
de03 ~~ de08 0.73 -0.03 -0.03 -0.04 -0.04
ut11 ~~ fa05 0.73 -0.03 -0.03 -0.05 -0.05
ut04 ~~ de01 0.73 -0.04 -0.04 -0.04 -0.04
co09 ~~ ut01 0.73 -0.02 -0.02 -0.04 -0.04
ut07 ~~ de07 0.73 -0.03 -0.03 -0.04 -0.04
pr06 ~~ fa08 0.72 -0.04 -0.04 -0.04 -0.04
pr02 ~~ un12 0.72 0.02 0.02 0.04 0.04
pr07 ~~ de08 0.71 -0.02 -0.02 -0.04 -0.04
co03 ~~ fa10 0.71 0.03 0.03 0.04 0.04
pr06 ~~ de05 0.71 0.03 0.03 0.04 0.04
pr05 ~~ ut06 0.70 -0.03 -0.03 -0.04 -0.04
ut11 ~~ de08 0.70 0.03 0.03 0.04 0.04
un01 ~~ un02 0.69 -0.02 -0.02 -0.04 -0.04
pr07 ~~ fa10 0.69 0.03 0.03 0.04 0.04
ut09 ~~ fa01 0.69 0.02 0.02 0.05 0.05
ut05 ~~ fa08 0.68 0.04 0.04 0.04 0.04
ut07 ~~ de05 0.67 -0.04 -0.04 -0.04 -0.04
pr08 ~~ un05 0.67 0.02 0.02 0.04 0.04
de02 ~~ un06 0.67 0.03 0.03 0.04 0.04
fa01 ~~ un12 0.66 -0.02 -0.02 -0.05 -0.05
co08 ~~ fa06 0.66 -0.03 -0.03 -0.04 -0.04
co08 ~~ fa10 0.66 -0.04 -0.04 -0.04 -0.04
un05 ~~ un10 0.65 0.02 0.02 0.04 0.04
ut07 ~~ un08 0.64 0.03 0.03 0.04 0.04
fa01 ~~ fa02 0.64 -0.03 -0.03 -0.05 -0.05
de06 ~~ de08 0.64 -0.03 -0.03 -0.04 -0.04
pr09 ~~ fa05 0.64 -0.02 -0.02 -0.04 -0.04
co03 ~~ ut02 0.63 0.02 0.02 0.04 0.04
fa10 ~~ de07 0.63 -0.03 -0.03 -0.04 -0.04
ut01 ~~ un11 0.62 -0.02 -0.02 -0.04 -0.04
pr08 ~~ co06 0.62 -0.02 -0.02 -0.04 -0.04
ut11 ~~ un10 0.62 0.03 0.03 0.04 0.04
ut08 ~~ fa08 0.62 -0.03 -0.03 -0.04 -0.04
de08 ~~ un08 0.62 -0.02 -0.02 -0.04 -0.04
pr01 ~~ fa10 0.62 -0.02 -0.02 -0.04 -0.04
ut11 ~~ un06 0.62 -0.04 -0.04 -0.04 -0.04
fa05 ~~ un07 0.61 0.02 0.02 0.04 0.04
de05 ~~ un09 0.61 -0.03 -0.03 -0.04 -0.04
de07 ~~ un07 0.61 0.03 0.03 0.04 0.04
co05 ~~ ut07 0.61 0.03 0.03 0.04 0.04
ut08 ~~ fa09 0.61 -0.03 -0.03 -0.04 -0.04
pr10 ~~ de03 0.60 -0.03 -0.03 -0.04 -0.04
pr09 ~~ de09 0.60 0.03 0.03 0.04 0.04
pr10 ~~ co03 0.60 0.03 0.03 0.04 0.04
fa01 ~~ de02 0.59 -0.02 -0.02 -0.04 -0.04
pr09 ~~ de06 0.59 -0.03 -0.03 -0.04 -0.04
pr02 ~~ ut07 0.59 -0.03 -0.03 -0.04 -0.04
ut12 ~~ un10 0.58 0.02 0.02 0.04 0.04
ut05 ~~ un12 0.58 -0.03 -0.03 -0.04 -0.04
ut12 ~~ un05 0.58 -0.02 -0.02 -0.04 -0.04
co02 ~~ de03 0.58 0.03 0.03 0.04 0.04
pr01 ~~ co06 0.58 -0.02 -0.02 -0.04 -0.04
co08 ~~ un08 0.57 -0.03 -0.03 -0.04 -0.04
pr05 ~~ pr07 0.57 -0.03 -0.03 -0.04 -0.04
pr05 ~~ pr10 0.57 -0.03 -0.03 -0.04 -0.04
co05 ~~ un05 0.57 -0.02 -0.02 -0.04 -0.04
co03 ~~ fa09 0.57 -0.03 -0.03 -0.04 -0.04
ut08 ~~ fa10 0.57 0.03 0.03 0.04 0.04
ut03 ~~ ut05 0.57 -0.03 -0.03 -0.04 -0.04
ut05 ~~ un02 0.56 -0.02 -0.02 -0.04 -0.04
fa10 ~~ un04 0.56 0.03 0.03 0.04 0.04
co01 ~~ fa10 0.56 -0.03 -0.03 -0.04 -0.04
co05 ~~ fa04 0.55 0.03 0.03 0.04 0.04
ut07 ~~ ut12 0.55 0.03 0.03 0.04 0.04
ut03 ~~ ut06 0.54 -0.03 -0.03 -0.04 -0.04
de08 ~~ un06 0.54 -0.03 -0.03 -0.04 -0.04
co10 ~~ ut12 0.54 0.03 0.03 0.04 0.04
pr06 ~~ ut03 0.53 -0.03 -0.03 -0.03 -0.03
pr05 ~~ de02 0.53 0.03 0.03 0.04 0.04
pr08 ~~ un04 0.53 -0.02 -0.02 -0.04 -0.04
co04 ~~ ut09 0.53 0.03 0.03 0.03 0.03
fa05 ~~ un11 0.52 -0.02 -0.02 -0.04 -0.04
ut02 ~~ un09 0.52 0.02 0.02 0.04 0.04
fa10 ~~ de06 0.52 -0.03 -0.03 -0.03 -0.03
de03 ~~ un01 0.52 -0.02 -0.02 -0.03 -0.03
pr09 ~~ un03 0.52 -0.03 -0.03 -0.03 -0.03
fa09 ~~ de10 0.52 0.03 0.03 0.03 0.03
pr02 ~~ co01 0.52 -0.02 -0.02 -0.04 -0.04
pr10 ~~ ut02 0.52 0.02 0.02 0.04 0.04
pr02 ~~ un01 0.51 -0.02 -0.02 -0.03 -0.03
co05 ~~ un06 0.51 0.03 0.03 0.04 0.04
ut11 ~~ un11 0.51 0.03 0.03 0.03 0.03
ut09 ~~ un08 0.51 0.02 0.02 0.03 0.03
pr09 ~~ un12 0.51 -0.02 -0.02 -0.03 -0.03
pr01 ~~ pr02 0.51 0.02 0.02 0.04 0.04
un03 ~~ un09 0.51 0.03 0.03 0.03 0.03
co01 ~~ ut05 0.50 0.02 0.02 0.04 0.04
pr01 ~~ un06 0.50 -0.02 -0.02 -0.03 -0.03
pr01 ~~ pr08 0.50 0.01 0.01 0.04 0.04
pr09 ~~ co05 0.50 -0.02 -0.02 -0.04 -0.04
ut04 ~~ un05 0.50 -0.02 -0.02 -0.03 -0.03
pr05 ~~ un12 0.50 0.03 0.03 0.03 0.03
ut12 ~~ un11 0.50 0.02 0.02 0.03 0.03
co04 ~~ fa05 0.49 0.02 0.02 0.04 0.04
ut09 ~~ un11 0.49 0.02 0.02 0.03 0.03
ut09 ~~ de09 0.49 -0.03 -0.03 -0.03 -0.03
co03 ~~ ut08 0.49 -0.02 -0.02 -0.03 -0.03
co05 ~~ de02 0.48 0.02 0.02 0.04 0.04
pr10 ~~ un04 0.48 -0.02 -0.02 -0.03 -0.03
co04 ~~ ut12 0.48 -0.03 -0.03 -0.03 -0.03
co10 ~~ de06 0.47 0.03 0.03 0.03 0.03
ut05 ~~ fa01 0.47 -0.02 -0.02 -0.04 -0.04
un04 ~~ un07 0.47 -0.02 -0.02 -0.03 -0.03
de02 ~~ de08 0.47 0.02 0.02 0.04 0.04
pr06 ~~ fa04 0.47 -0.03 -0.03 -0.03 -0.03
pr07 ~~ fa08 0.46 0.03 0.03 0.03 0.03
co04 ~~ de08 0.46 0.03 0.03 0.03 0.03
pr08 ~~ fa06 0.46 0.02 0.02 0.03 0.03
co05 ~~ fa01 0.45 -0.02 -0.02 -0.04 -0.04
pr08 ~~ de10 0.45 0.02 0.02 0.03 0.03
pr02 ~~ pr06 0.45 0.02 0.02 0.03 0.03
pr07 ~~ ut12 0.45 0.02 0.02 0.03 0.03
ut07 ~~ fa09 0.45 -0.03 -0.03 -0.03 -0.03
fa01 ~~ fa04 0.44 0.02 0.02 0.04 0.04
co04 ~~ ut07 0.44 -0.03 -0.03 -0.03 -0.03
ut08 ~~ un11 0.44 -0.02 -0.02 -0.03 -0.03
de07 ~~ un04 0.44 0.02 0.02 0.03 0.03
co01 ~~ un06 0.44 0.03 0.03 0.03 0.03
un03 ~~ un06 0.44 -0.03 -0.03 -0.03 -0.03
fa04 ~~ un03 0.43 0.03 0.03 0.03 0.03
co03 ~~ ut07 0.43 0.03 0.03 0.03 0.03
ut01 ~~ un04 0.43 0.02 0.02 0.03 0.03
pr10 ~~ de08 0.42 -0.02 -0.02 -0.03 -0.03
ut05 ~~ de07 0.42 0.02 0.02 0.03 0.03
de03 ~~ un03 0.42 0.03 0.03 0.03 0.03
co08 ~~ un09 0.42 0.03 0.03 0.03 0.03
pr02 ~~ ut02 0.42 -0.02 -0.02 -0.03 -0.03
co10 ~~ ut07 0.41 0.02 0.02 0.03 0.03
ut12 ~~ fa08 0.41 0.03 0.03 0.03 0.03
co03 ~~ un07 0.41 -0.02 -0.02 -0.03 -0.03
ut12 ~~ un07 0.41 0.02 0.02 0.03 0.03
co01 ~~ un10 0.41 -0.02 -0.02 -0.03 -0.03
co06 ~~ ut03 0.41 -0.03 -0.03 -0.03 -0.03
co01 ~~ ut03 0.40 0.02 0.02 0.03 0.03
co06 ~~ un02 0.40 0.02 0.02 0.03 0.03
pr06 ~~ co06 0.40 0.02 0.02 0.03 0.03
pr09 ~~ ut01 0.40 -0.02 -0.02 -0.03 -0.03
pr08 ~~ ut07 0.39 -0.02 -0.02 -0.03 -0.03
co03 ~~ ut06 0.39 0.02 0.02 0.03 0.03
ut06 ~~ fa05 0.39 0.02 0.02 0.04 0.04
de03 ~~ un05 0.39 -0.02 -0.02 -0.03 -0.03
un01 ~~ un04 0.39 -0.02 -0.02 -0.03 -0.03
pr02 ~~ de09 0.39 0.03 0.03 0.03 0.03
co02 ~~ ut01 0.39 0.02 0.02 0.03 0.03
co09 ~~ un04 0.39 0.02 0.02 0.03 0.03
ut06 ~~ un09 0.38 -0.02 -0.02 -0.03 -0.03
co01 ~~ ut09 0.38 0.02 0.02 0.03 0.03
ut09 ~~ fa06 0.38 -0.02 -0.02 -0.03 -0.03
pr01 ~~ pr06 0.38 0.02 0.02 0.03 0.03
un10 ~~ un12 0.38 -0.02 -0.02 -0.03 -0.03
de06 ~~ un12 0.37 -0.02 -0.02 -0.03 -0.03
pr09 ~~ fa10 0.37 -0.02 -0.02 -0.03 -0.03
fa01 ~~ fa09 0.37 0.02 0.02 0.04 0.04
pr08 ~~ un03 0.37 0.02 0.02 0.03 0.03
ut08 ~~ de08 0.37 -0.02 -0.02 -0.03 -0.03
pr02 ~~ pr09 0.37 -0.02 -0.02 -0.03 -0.03
de07 ~~ un05 0.37 0.02 0.02 0.03 0.03
ut06 ~~ de10 0.36 -0.02 -0.02 -0.03 -0.03
co08 ~~ ut08 0.36 -0.02 -0.02 -0.03 -0.03
un10 ~~ un11 0.36 0.02 0.02 0.03 0.03
fa05 ~~ de07 0.36 -0.02 -0.02 -0.03 -0.03
pr08 ~~ co01 0.36 -0.01 -0.01 -0.03 -0.03
ut06 ~~ un10 0.36 -0.02 -0.02 -0.03 -0.03
co09 ~~ un10 0.36 0.02 0.02 0.03 0.03
co03 ~~ de06 0.35 -0.02 -0.02 -0.03 -0.03
fa10 ~~ un11 0.35 -0.02 -0.02 -0.03 -0.03
ut09 ~~ un01 0.35 0.02 0.02 0.03 0.03
fa05 ~~ un02 0.35 -0.01 -0.01 -0.03 -0.03
co01 ~~ co02 0.35 -0.02 -0.02 -0.03 -0.03
fa06 ~~ un12 0.35 -0.02 -0.02 -0.03 -0.03
pr10 ~~ co01 0.35 -0.02 -0.02 -0.03 -0.03
ut08 ~~ un03 0.35 0.02 0.02 0.03 0.03
de01 ~~ un11 0.34 0.02 0.02 0.03 0.03
pr01 ~~ ut04 0.34 -0.02 -0.02 -0.03 -0.03
fa06 ~~ de07 0.34 0.02 0.02 0.03 0.03
pr10 ~~ fa10 0.34 -0.02 -0.02 -0.03 -0.03
fa10 ~~ de03 0.33 -0.03 -0.03 -0.03 -0.03
co03 ~~ fa08 0.33 0.02 0.02 0.03 0.03
pr09 ~~ un02 0.33 0.01 0.01 0.03 0.03
un04 ~~ un05 0.33 -0.01 -0.01 -0.03 -0.03
de01 ~~ un12 0.33 -0.02 -0.02 -0.03 -0.03
ut11 ~~ ut12 0.32 0.02 0.02 0.03 0.03
de05 ~~ de08 0.32 -0.02 -0.02 -0.03 -0.03
co10 ~~ de05 0.32 0.02 0.02 0.03 0.03
pr07 ~~ ut04 0.32 -0.02 -0.02 -0.03 -0.03
pr02 ~~ ut08 0.32 0.02 0.02 0.03 0.03
de02 ~~ de06 0.31 0.02 0.02 0.03 0.03
pr08 ~~ ut01 0.31 0.01 0.01 0.03 0.03
co02 ~~ de06 0.31 -0.02 -0.02 -0.03 -0.03
ut06 ~~ ut11 0.31 0.02 0.02 0.03 0.03
co09 ~~ de03 0.31 0.02 0.02 0.03 0.03
pr09 ~~ fa06 0.30 0.02 0.02 0.03 0.03
co05 ~~ fa02 0.30 -0.02 -0.02 -0.03 -0.03
co08 ~~ ut01 0.30 -0.02 -0.02 -0.03 -0.03
pr09 ~~ un06 0.30 -0.02 -0.02 -0.03 -0.03
co04 ~~ fa08 0.30 -0.03 -0.03 -0.03 -0.03
pr08 ~~ ut08 0.30 0.01 0.01 0.03 0.03
de06 ~~ un06 0.29 0.03 0.03 0.03 0.03
de01 ~~ un03 0.29 -0.02 -0.02 -0.03 -0.03
co01 ~~ ut06 0.29 -0.01 -0.01 -0.03 -0.03
co05 ~~ ut09 0.29 -0.02 -0.02 -0.03 -0.03
de07 ~~ un08 0.29 -0.02 -0.02 -0.03 -0.03
pr06 ~~ ut02 0.29 -0.01 -0.01 -0.03 -0.03
co02 ~~ ut11 0.28 -0.02 -0.02 -0.03 -0.03
pr01 ~~ ut02 0.28 0.01 0.01 0.03 0.03
fa01 ~~ un09 0.28 -0.02 -0.02 -0.03 -0.03
pr02 ~~ ut11 0.28 0.02 0.02 0.02 0.02
co10 ~~ fa05 0.28 0.02 0.02 0.03 0.03
ut08 ~~ de09 0.28 -0.02 -0.02 -0.02 -0.02
pr08 ~~ de07 0.28 0.01 0.01 0.03 0.03
co06 ~~ ut05 0.28 0.02 0.02 0.03 0.03
pr02 ~~ fa01 0.28 0.01 0.01 0.03 0.03
co02 ~~ fa04 0.27 0.02 0.02 0.02 0.02
ut08 ~~ ut09 0.27 0.02 0.02 0.03 0.03
fa06 ~~ un05 0.27 -0.01 -0.01 -0.03 -0.03
pr09 ~~ fa09 0.27 -0.02 -0.02 -0.02 -0.02
ut06 ~~ fa01 0.26 0.01 0.01 0.03 0.03
pr06 ~~ co03 0.26 0.02 0.02 0.02 0.02
pr08 ~~ fa02 0.26 0.02 0.02 0.02 0.02
pr09 ~~ co03 0.26 -0.02 -0.02 -0.02 -0.02
pr05 ~~ ut03 0.26 -0.02 -0.02 -0.02 -0.02
pr09 ~~ co06 0.26 -0.02 -0.02 -0.02 -0.02
de01 ~~ un10 0.26 -0.02 -0.02 -0.02 -0.02
co01 ~~ de08 0.26 0.01 0.01 0.03 0.03
ut06 ~~ un02 0.26 0.01 0.01 0.03 0.03
pr06 ~~ fa09 0.26 -0.02 -0.02 -0.02 -0.02
ut04 ~~ fa02 0.26 0.02 0.02 0.02 0.02
fa08 ~~ un12 0.26 0.02 0.02 0.02 0.02
pr02 ~~ de07 0.26 -0.01 -0.01 -0.02 -0.02
de10 ~~ un05 0.26 -0.01 -0.01 -0.03 -0.03
fa01 ~~ un05 0.25 -0.01 -0.01 -0.03 -0.03
pr06 ~~ fa10 0.25 -0.02 -0.02 -0.02 -0.02
un03 ~~ un04 0.25 0.02 0.02 0.02 0.02
ut11 ~~ un03 0.25 -0.02 -0.02 -0.02 -0.02
pr07 ~~ fa05 0.25 0.01 0.01 0.03 0.03
ut08 ~~ fa01 0.25 0.01 0.01 0.03 0.03
co05 ~~ ut05 0.25 -0.02 -0.02 -0.03 -0.03
ut04 ~~ ut12 0.25 -0.02 -0.02 -0.02 -0.02
pr09 ~~ co02 0.24 0.01 0.01 0.02 0.02
co04 ~~ co06 0.24 -0.02 -0.02 -0.02 -0.02
pr02 ~~ fa08 0.24 0.02 0.02 0.02 0.02
ut07 ~~ un05 0.24 -0.01 -0.01 -0.02 -0.02
co08 ~~ un03 0.24 -0.02 -0.02 -0.02 -0.02
pr02 ~~ co09 0.24 -0.01 -0.01 -0.02 -0.02
co10 ~~ fa01 0.24 -0.01 -0.01 -0.03 -0.03
pr06 ~~ un02 0.24 -0.01 -0.01 -0.02 -0.02
pr02 ~~ de08 0.24 -0.01 -0.01 -0.02 -0.02
co05 ~~ fa10 0.24 0.02 0.02 0.03 0.03
ut11 ~~ de07 0.23 -0.02 -0.02 -0.02 -0.02
co10 ~~ fa10 0.23 -0.02 -0.02 -0.02 -0.02
de01 ~~ un01 0.23 -0.01 -0.01 -0.02 -0.02
co08 ~~ ut06 0.22 0.02 0.02 0.02 0.02
ut01 ~~ fa01 0.22 -0.01 -0.01 -0.03 -0.03
pr06 ~~ pr08 0.22 0.01 0.01 0.02 0.02
co01 ~~ co09 0.22 -0.01 -0.01 -0.03 -0.03
ut09 ~~ de10 0.22 0.02 0.02 0.02 0.02
pr08 ~~ co05 0.22 0.01 0.01 0.02 0.02
de09 ~~ un01 0.22 0.02 0.02 0.02 0.02
pr10 ~~ fa04 0.21 0.02 0.02 0.02 0.02
pr09 ~~ ut03 0.21 0.02 0.02 0.02 0.02
fa02 ~~ un07 0.21 -0.02 -0.02 -0.02 -0.02
pr07 ~~ co05 0.21 0.01 0.01 0.02 0.02
ut02 ~~ de08 0.21 0.01 0.01 0.02 0.02
ut05 ~~ de06 0.21 -0.02 -0.02 -0.02 -0.02
ut11 ~~ de03 0.21 0.02 0.02 0.02 0.02
pr02 ~~ un03 0.21 -0.02 -0.02 -0.02 -0.02
pr07 ~~ de09 0.21 -0.02 -0.02 -0.02 -0.02
un02 ~~ un09 0.21 0.01 0.01 0.02 0.02
ut05 ~~ ut06 0.21 -0.02 -0.02 -0.02 -0.02
pr05 ~~ un04 0.21 0.02 0.02 0.02 0.02
de06 ~~ un11 0.21 0.02 0.02 0.02 0.02
co03 ~~ un03 0.21 0.02 0.02 0.02 0.02
pr10 ~~ de06 0.20 -0.02 -0.02 -0.02 -0.02
co09 ~~ ut02 0.20 0.01 0.01 0.02 0.02
fa02 ~~ un02 0.20 0.02 0.02 0.02 0.02
co04 ~~ ut06 0.20 0.02 0.02 0.02 0.02
ut03 ~~ fa01 0.20 -0.01 -0.01 -0.02 -0.02
co08 ~~ de06 0.20 -0.02 -0.02 -0.02 -0.02
ut04 ~~ un10 0.20 -0.02 -0.02 -0.02 -0.02
ut11 ~~ fa08 0.19 -0.02 -0.02 -0.02 -0.02
fa02 ~~ un11 0.19 0.02 0.02 0.02 0.02
pr05 ~~ fa01 0.19 0.01 0.01 0.02 0.02
pr07 ~~ un05 0.18 -0.01 -0.01 -0.02 -0.02
fa01 ~~ fa06 0.18 -0.01 -0.01 -0.03 -0.03
co08 ~~ de01 0.18 0.02 0.02 0.02 0.02
pr09 ~~ ut04 0.18 -0.02 -0.02 -0.02 -0.02
ut06 ~~ un11 0.18 -0.01 -0.01 -0.02 -0.02
pr01 ~~ un08 0.18 -0.01 -0.01 -0.02 -0.02
co02 ~~ ut07 0.18 0.01 0.01 0.02 0.02
pr01 ~~ un03 0.18 0.01 0.01 0.02 0.02
fa09 ~~ de01 0.18 -0.02 -0.02 -0.02 -0.02
ut05 ~~ un09 0.18 -0.02 -0.02 -0.02 -0.02
pr01 ~~ pr07 0.18 -0.01 -0.01 -0.02 -0.02
pr08 ~~ de01 0.17 -0.01 -0.01 -0.02 -0.02
ut07 ~~ un02 0.17 -0.01 -0.01 -0.02 -0.02
ut12 ~~ de02 0.17 0.01 0.01 0.02 0.02
de01 ~~ un09 0.17 -0.02 -0.02 -0.02 -0.02
fa05 ~~ fa08 0.17 -0.01 -0.01 -0.02 -0.02
ut01 ~~ fa02 0.17 -0.01 -0.01 -0.02 -0.02
de07 ~~ de10 0.17 -0.01 -0.01 -0.02 -0.02
fa02 ~~ un05 0.17 -0.01 -0.01 -0.02 -0.02
co02 ~~ fa08 0.17 0.02 0.02 0.02 0.02
fa05 ~~ de08 0.17 0.01 0.01 0.02 0.02
ut01 ~~ un02 0.17 -0.01 -0.01 -0.02 -0.02
pr05 ~~ de07 0.17 -0.02 -0.02 -0.02 -0.02
pr01 ~~ ut06 0.16 -0.01 -0.01 -0.02 -0.02
ut02 ~~ ut05 0.16 -0.01 -0.01 -0.02 -0.02
fa01 ~~ un02 0.16 0.01 0.01 0.02 0.02
pr10 ~~ un05 0.16 0.01 0.01 0.02 0.02
pr05 ~~ co08 0.16 0.02 0.02 0.02 0.02
de08 ~~ un03 0.16 0.01 0.01 0.02 0.02
co06 ~~ fa09 0.16 0.02 0.02 0.02 0.02
co06 ~~ ut06 0.16 0.01 0.01 0.02 0.02
ut04 ~~ ut11 0.16 0.02 0.02 0.02 0.02
co09 ~~ ut07 0.16 0.01 0.01 0.02 0.02
pr08 ~~ ut12 0.16 -0.01 -0.01 -0.02 -0.02
ut06 ~~ un01 0.16 -0.01 -0.01 -0.02 -0.02
fa09 ~~ de02 0.16 -0.02 -0.02 -0.02 -0.02
ut05 ~~ un06 0.16 -0.02 -0.02 -0.02 -0.02
co06 ~~ de05 0.16 0.02 0.02 0.02 0.02
pr06 ~~ de01 0.15 0.01 0.01 0.02 0.02
pr02 ~~ de10 0.15 -0.01 -0.01 -0.02 -0.02
ut09 ~~ de05 0.15 -0.02 -0.02 -0.02 -0.02
pr08 ~~ co02 0.15 0.01 0.01 0.02 0.02
fa08 ~~ de03 0.15 -0.02 -0.02 -0.02 -0.02
pr05 ~~ co02 0.15 0.01 0.01 0.02 0.02
fa01 ~~ fa05 0.15 -0.01 -0.01 -0.04 -0.04
co08 ~~ ut04 0.15 -0.02 -0.02 -0.02 -0.02
co10 ~~ un07 0.15 -0.01 -0.01 -0.02 -0.02
de01 ~~ un06 0.15 -0.02 -0.02 -0.02 -0.02
pr02 ~~ ut05 0.15 -0.01 -0.01 -0.02 -0.02
de01 ~~ de10 0.15 -0.01 -0.01 -0.02 -0.02
pr01 ~~ de03 0.15 -0.01 -0.01 -0.02 -0.02
co05 ~~ ut12 0.15 0.01 0.01 0.02 0.02
pr01 ~~ ut08 0.15 0.01 0.01 0.02 0.02
pr06 ~~ un06 0.15 -0.02 -0.02 -0.02 -0.02
ut11 ~~ de09 0.14 -0.02 -0.02 -0.02 -0.02
pr06 ~~ un07 0.14 0.01 0.01 0.02 0.02
co06 ~~ co08 0.14 -0.02 -0.02 -0.02 -0.02
fa01 ~~ de08 0.14 0.01 0.01 0.02 0.02
co08 ~~ ut12 0.14 -0.01 -0.01 -0.02 -0.02
pr07 ~~ fa06 0.14 -0.01 -0.01 -0.02 -0.02
fa01 ~~ un03 0.14 -0.01 -0.01 -0.02 -0.02
ut12 ~~ un08 0.14 0.01 0.01 0.02 0.02
ut04 ~~ ut06 0.14 -0.01 -0.01 -0.02 -0.02
co09 ~~ ut08 0.14 0.01 0.01 0.02 0.02
pr06 ~~ ut09 0.14 -0.01 -0.01 -0.02 -0.02
ut07 ~~ fa06 0.13 0.01 0.01 0.02 0.02
fa09 ~~ de05 0.13 0.02 0.02 0.02 0.02
fa10 ~~ de10 0.13 -0.01 -0.01 -0.02 -0.02
pr06 ~~ pr07 0.13 0.01 0.01 0.02 0.02
fa08 ~~ un01 0.13 0.01 0.01 0.02 0.02
ut03 ~~ ut12 0.13 -0.01 -0.01 -0.02 -0.02
fa05 ~~ un01 0.13 0.01 0.01 0.02 0.02
ut04 ~~ un01 0.13 0.01 0.01 0.02 0.02
co10 ~~ un05 0.13 -0.01 -0.01 -0.02 -0.02
pr05 ~~ de08 0.13 -0.01 -0.01 -0.02 -0.02
co03 ~~ fa02 0.13 -0.02 -0.02 -0.02 -0.02
pr02 ~~ un06 0.12 0.01 0.01 0.02 0.02
co05 ~~ fa08 0.12 -0.01 -0.01 -0.02 -0.02
pr02 ~~ de05 0.12 0.01 0.01 0.02 0.02
pr09 ~~ co01 0.12 0.01 0.01 0.02 0.02
co04 ~~ fa06 0.12 -0.01 -0.01 -0.02 -0.02
pr01 ~~ ut11 0.12 -0.01 -0.01 -0.02 -0.02
pr07 ~~ un09 0.12 0.01 0.01 0.02 0.02
co05 ~~ de06 0.12 -0.01 -0.01 -0.02 -0.02
co09 ~~ fa06 0.12 -0.01 -0.01 -0.02 -0.02
pr09 ~~ de03 0.12 0.01 0.01 0.02 0.02
pr01 ~~ de08 0.12 -0.01 -0.01 -0.02 -0.02
co06 ~~ ut11 0.12 0.01 0.01 0.02 0.02
de09 ~~ un09 0.12 -0.02 -0.02 -0.02 -0.02
un05 ~~ un12 0.12 0.01 0.01 0.02 0.02
pr10 ~~ de02 0.12 0.01 0.01 0.02 0.02
co09 ~~ fa05 0.11 -0.01 -0.01 -0.02 -0.02
pr08 ~~ un10 0.11 -0.01 -0.01 -0.02 -0.02
ut09 ~~ de02 0.11 0.01 0.01 0.02 0.02
ut08 ~~ un04 0.11 -0.01 -0.01 -0.02 -0.02
co09 ~~ fa08 0.11 0.01 0.01 0.02 0.02
co02 ~~ ut12 0.11 0.01 0.01 0.02 0.02
de08 ~~ un10 0.11 -0.01 -0.01 -0.02 -0.02
pr01 ~~ pr10 0.11 0.01 0.01 0.02 0.02
co05 ~~ fa06 0.11 0.01 0.01 0.02 0.02
co06 ~~ de01 0.11 -0.01 -0.01 -0.02 -0.02
ut07 ~~ un10 0.11 -0.01 -0.01 -0.02 -0.02
un02 ~~ un05 0.11 -0.01 -0.01 -0.02 -0.02
ut08 ~~ un05 0.10 0.01 0.01 0.02 0.02
fa05 ~~ un09 0.10 0.01 0.01 0.02 0.02
fa09 ~~ un05 0.10 -0.01 -0.01 -0.02 -0.02
fa08 ~~ un05 0.10 0.01 0.01 0.02 0.02
fa10 ~~ de01 0.10 0.01 0.01 0.02 0.02
ut12 ~~ un02 0.10 -0.01 -0.01 -0.02 -0.02
ut03 ~~ fa05 0.10 0.01 0.01 0.02 0.02
ut03 ~~ un02 0.10 0.01 0.01 0.02 0.02
pr02 ~~ un04 0.10 0.01 0.01 0.02 0.02
ut05 ~~ fa05 0.10 0.01 0.01 0.02 0.02
ut02 ~~ ut09 0.10 0.01 0.01 0.02 0.02
pr05 ~~ co06 0.10 0.01 0.01 0.01 0.01
co10 ~~ un04 0.10 -0.01 -0.01 -0.02 -0.02
pr02 ~~ pr10 0.09 0.01 0.01 0.01 0.01
fa02 ~~ de03 0.09 0.01 0.01 0.01 0.01
co06 ~~ un04 0.09 0.01 0.01 0.01 0.01
pr01 ~~ fa05 0.09 -0.01 -0.01 -0.02 -0.02
co02 ~~ un08 0.09 -0.01 -0.01 -0.01 -0.01
co09 ~~ ut11 0.09 0.01 0.01 0.01 0.01
fa06 ~~ un04 0.09 0.01 0.01 0.01 0.01
pr07 ~~ fa09 0.09 0.01 0.01 0.01 0.01
fa02 ~~ de01 0.09 -0.01 -0.01 -0.01 -0.01
co08 ~~ ut09 0.09 0.01 0.01 0.01 0.01
co10 ~~ ut04 0.09 -0.01 -0.01 -0.01 -0.01
fa10 ~~ de08 0.09 -0.01 -0.01 -0.01 -0.01
pr06 ~~ pr09 0.09 -0.01 -0.01 -0.01 -0.01
co01 ~~ co03 0.09 -0.01 -0.01 -0.01 -0.01
pr05 ~~ ut09 0.08 0.01 0.01 0.01 0.01
pr01 ~~ co01 0.08 0.01 0.01 0.02 0.02
co05 ~~ fa05 0.08 0.01 0.01 0.02 0.02
co10 ~~ de02 0.08 0.01 0.01 0.01 0.01
pr05 ~~ de03 0.08 -0.01 -0.01 -0.01 -0.01
co09 ~~ un02 0.08 -0.01 -0.01 -0.01 -0.01
ut02 ~~ un08 0.08 0.01 0.01 0.01 0.01
co10 ~~ de09 0.08 -0.01 -0.01 -0.01 -0.01
co02 ~~ un03 0.08 -0.01 -0.01 -0.01 -0.01
ut07 ~~ un12 0.08 0.01 0.01 0.01 0.01
ut12 ~~ de08 0.08 0.01 0.01 0.01 0.01
co06 ~~ ut04 0.08 -0.01 -0.01 -0.01 -0.01
pr02 ~~ ut12 0.08 0.01 0.01 0.01 0.01
ut02 ~~ fa05 0.08 -0.01 -0.01 -0.02 -0.02
ut04 ~~ de05 0.08 -0.01 -0.01 -0.01 -0.01
ut01 ~~ un08 0.07 0.01 0.01 0.01 0.01
pr06 ~~ ut04 0.07 0.01 0.01 0.01 0.01
co03 ~~ un09 0.07 -0.01 -0.01 -0.01 -0.01
co06 ~~ de06 0.07 0.01 0.01 0.01 0.01
ut01 ~~ un06 0.07 -0.01 -0.01 -0.01 -0.01
pr06 ~~ ut07 0.07 0.01 0.01 0.01 0.01
pr10 ~~ de07 0.07 -0.01 -0.01 -0.01 -0.01
de03 ~~ un08 0.07 0.01 0.01 0.01 0.01
fa06 ~~ de03 0.07 -0.01 -0.01 -0.01 -0.01
pr01 ~~ un05 0.07 0.01 0.01 0.01 0.01
co05 ~~ de03 0.07 -0.01 -0.01 -0.01 -0.01
ut06 ~~ fa10 0.07 -0.01 -0.01 -0.01 -0.01
ut04 ~~ un07 0.06 0.01 0.01 0.01 0.01
co03 ~~ un11 0.06 -0.01 -0.01 -0.01 -0.01
fa06 ~~ de01 0.06 0.01 0.01 0.01 0.01
ut05 ~~ un03 0.06 -0.01 -0.01 -0.01 -0.01
ut04 ~~ un03 0.06 -0.01 -0.01 -0.01 -0.01
ut11 ~~ fa01 0.06 0.01 0.01 0.01 0.01
pr07 ~~ co06 0.06 -0.01 -0.01 -0.01 -0.01
un09 ~~ un12 0.06 0.01 0.01 0.01 0.01
ut03 ~~ fa10 0.06 0.01 0.01 0.01 0.01
co02 ~~ un01 0.06 0.01 0.01 0.01 0.01
co06 ~~ de08 0.06 0.01 0.01 0.01 0.01
co02 ~~ ut08 0.06 0.01 0.01 0.01 0.01
co08 ~~ ut11 0.06 -0.01 -0.01 -0.01 -0.01
co08 ~~ un04 0.06 0.01 0.01 0.01 0.01
pr10 ~~ ut05 0.06 -0.01 -0.01 -0.01 -0.01
co05 ~~ ut08 0.06 -0.01 -0.01 -0.01 -0.01
co01 ~~ un05 0.05 -0.01 -0.01 -0.01 -0.01
pr10 ~~ un06 0.05 -0.01 -0.01 -0.01 -0.01
pr10 ~~ un01 0.05 0.01 0.01 0.01 0.01
co02 ~~ fa10 0.05 0.01 0.01 0.01 0.01
co08 ~~ un02 0.05 -0.01 -0.01 -0.01 -0.01
fa02 ~~ fa05 0.05 -0.01 -0.01 -0.01 -0.01
co06 ~~ un12 0.05 0.01 0.01 0.01 0.01
pr06 ~~ ut12 0.05 -0.01 -0.01 -0.01 -0.01
pr07 ~~ fa01 0.05 -0.01 -0.01 -0.01 -0.01
co01 ~~ co08 0.05 0.01 0.01 0.01 0.01
de08 ~~ un02 0.05 -0.01 -0.01 -0.01 -0.01
ut11 ~~ de02 0.05 0.01 0.01 0.01 0.01
pr02 ~~ fa06 0.05 -0.01 -0.01 -0.01 -0.01
co09 ~~ un03 0.04 -0.01 -0.01 -0.01 -0.01
pr08 ~~ ut09 0.04 -0.01 -0.01 -0.01 -0.01
pr06 ~~ fa06 0.04 -0.01 -0.01 -0.01 -0.01
ut11 ~~ un02 0.04 -0.01 -0.01 -0.01 -0.01
pr06 ~~ ut05 0.04 -0.01 -0.01 -0.01 -0.01
pr09 ~~ pr10 0.04 -0.01 -0.01 -0.01 -0.01
ut06 ~~ fa06 0.04 -0.01 -0.01 -0.01 -0.01
pr02 ~~ de02 0.04 0.01 0.01 0.01 0.01
ut12 ~~ de09 0.04 0.01 0.01 0.01 0.01
pr08 ~~ ut06 0.04 0.00 0.00 0.01 0.01
ut12 ~~ un06 0.04 0.01 0.01 0.01 0.01
pr07 ~~ un07 0.04 0.01 0.01 0.01 0.01
un02 ~~ un10 0.04 0.01 0.01 0.01 0.01
ut12 ~~ de05 0.04 -0.01 -0.01 -0.01 -0.01
ut02 ~~ fa08 0.04 0.01 0.01 0.01 0.01
co03 ~~ ut05 0.04 -0.01 -0.01 -0.01 -0.01
ut06 ~~ de09 0.04 0.01 0.01 0.01 0.01
pr02 ~~ ut01 0.04 0.00 0.00 -0.01 -0.01
pr02 ~~ fa09 0.04 -0.01 -0.01 -0.01 -0.01
ut09 ~~ fa09 0.03 -0.01 -0.01 -0.01 -0.01
pr01 ~~ ut07 0.03 -0.01 -0.01 -0.01 -0.01
fa06 ~~ de06 0.03 0.01 0.01 0.01 0.01
fa02 ~~ un12 0.03 -0.01 -0.01 -0.01 -0.01
co02 ~~ fa02 0.03 0.01 0.01 0.01 0.01
co09 ~~ fa09 0.03 -0.01 -0.01 -0.01 -0.01
co02 ~~ un05 0.03 0.00 0.00 0.01 0.01
co01 ~~ un07 0.03 0.01 0.01 0.01 0.01
pr01 ~~ ut01 0.03 0.00 0.00 -0.01 -0.01
co10 ~~ de01 0.03 0.01 0.01 0.01 0.01
pr09 ~~ un07 0.03 0.01 0.01 0.01 0.01
pr02 ~~ un11 0.03 0.01 0.01 0.01 0.01
ut09 ~~ fa10 0.03 0.01 0.01 0.01 0.01
co01 ~~ ut02 0.03 0.00 0.00 -0.01 -0.01
ut06 ~~ de06 0.03 0.01 0.01 0.01 0.01
ut08 ~~ ut12 0.03 0.01 0.01 0.01 0.01
de06 ~~ un10 0.03 -0.01 -0.01 -0.01 -0.01
co02 ~~ un11 0.03 0.00 0.00 -0.01 -0.01
ut05 ~~ ut09 0.03 0.01 0.01 0.01 0.01
ut05 ~~ de02 0.03 0.01 0.01 0.01 0.01
co10 ~~ ut05 0.03 0.01 0.01 0.01 0.01
ut03 ~~ ut04 0.03 -0.01 -0.01 -0.01 -0.01
ut06 ~~ un04 0.03 0.00 0.00 -0.01 -0.01
pr06 ~~ co08 0.03 0.01 0.01 0.01 0.01
pr10 ~~ co09 0.03 -0.01 -0.01 -0.01 -0.01
co10 ~~ un09 0.03 -0.01 -0.01 -0.01 -0.01
pr07 ~~ un06 0.03 -0.01 -0.01 -0.01 -0.01
fa01 ~~ de09 0.02 0.01 0.01 0.01 0.01
pr07 ~~ co01 0.02 0.00 0.00 0.01 0.01
pr05 ~~ co05 0.02 0.01 0.01 0.01 0.01
co10 ~~ un03 0.02 0.01 0.01 0.01 0.01
pr10 ~~ ut08 0.02 -0.01 -0.01 -0.01 -0.01
co05 ~~ ut03 0.02 -0.01 -0.01 -0.01 -0.01
ut02 ~~ fa10 0.02 0.00 0.00 -0.01 -0.01
ut09 ~~ un12 0.02 0.00 0.00 -0.01 -0.01
pr02 ~~ co04 0.02 0.01 0.01 0.01 0.01
ut03 ~~ fa06 0.02 0.01 0.01 0.01 0.01
pr06 ~~ co01 0.02 0.00 0.00 0.01 0.01
de03 ~~ un06 0.02 0.01 0.01 0.01 0.01
co09 ~~ ut09 0.02 0.00 0.00 -0.01 -0.01
ut05 ~~ fa02 0.02 -0.01 -0.01 -0.01 -0.01
co01 ~~ fa01 0.02 0.00 0.00 -0.01 -0.01
pr10 ~~ co08 0.02 0.01 0.01 0.01 0.01
fa05 ~~ de06 0.02 0.00 0.00 0.01 0.01
pr05 ~~ ut04 0.02 -0.01 -0.01 -0.01 -0.01
pr06 ~~ un10 0.02 0.00 0.00 -0.01 -0.01
fa04 ~~ un06 0.02 0.01 0.01 0.01 0.01
pr02 ~~ un05 0.02 0.00 0.00 -0.01 -0.01
ut01 ~~ de03 0.02 0.00 0.00 -0.01 -0.01
pr07 ~~ co10 0.02 0.00 0.00 -0.01 -0.01
ut06 ~~ de03 0.02 0.00 0.00 -0.01 -0.01
pr06 ~~ pr10 0.02 0.00 0.00 -0.01 -0.01
ut02 ~~ de06 0.02 0.00 0.00 0.01 0.01
de05 ~~ un04 0.02 0.00 0.00 0.01 0.01
de06 ~~ un08 0.02 0.00 0.00 -0.01 -0.01
fa08 ~~ un10 0.02 0.01 0.01 0.01 0.01
ut02 ~~ un04 0.02 0.00 0.00 -0.01 -0.01
ut04 ~~ ut09 0.02 -0.01 -0.01 -0.01 -0.01
pr05 ~~ co10 0.02 -0.01 -0.01 -0.01 -0.01
fa06 ~~ de08 0.02 0.00 0.00 0.01 0.01
ut09 ~~ un07 0.02 0.00 0.00 0.01 0.01
pr10 ~~ fa09 0.02 -0.01 -0.01 -0.01 -0.01
ut09 ~~ de01 0.01 0.00 0.00 0.01 0.01
co02 ~~ de01 0.01 0.00 0.00 -0.01 -0.01
ut12 ~~ fa04 0.01 0.00 0.00 0.01 0.01
co02 ~~ de05 0.01 0.00 0.00 -0.01 -0.01
ut11 ~~ fa09 0.01 0.01 0.01 0.01 0.01
fa06 ~~ de09 0.01 0.01 0.01 0.01 0.01
ut07 ~~ un11 0.01 0.00 0.00 -0.01 -0.01
ut02 ~~ un02 0.01 0.00 0.00 0.01 0.01
ut05 ~~ ut07 0.01 0.00 0.00 0.00 0.00
co01 ~~ co04 0.01 0.00 0.00 0.00 0.00
ut07 ~~ fa04 0.01 0.00 0.00 0.00 0.00
pr07 ~~ ut11 0.01 0.00 0.00 0.00 0.00
pr07 ~~ de07 0.01 0.00 0.00 0.00 0.00
co02 ~~ un04 0.01 0.00 0.00 0.00 0.00
fa01 ~~ un04 0.01 0.00 0.00 0.01 0.01
ut03 ~~ de07 0.01 0.00 0.00 0.00 0.00
ut12 ~~ un12 0.01 0.00 0.00 0.00 0.00
co01 ~~ de05 0.01 0.00 0.00 0.00 0.00
co10 ~~ un06 0.01 0.00 0.00 0.00 0.00
ut07 ~~ un01 0.01 0.00 0.00 0.00 0.00
ut12 ~~ fa05 0.01 0.00 0.00 0.00 0.00
pr05 ~~ ut02 0.01 0.00 0.00 0.00 0.00
pr07 ~~ un08 0.01 0.00 0.00 0.00 0.00
co06 ~~ un06 0.01 0.00 0.00 0.00 0.00
co10 ~~ ut09 0.01 0.00 0.00 0.00 0.00
pr10 ~~ ut12 0.01 0.00 0.00 0.00 0.00
de08 ~~ de09 0.01 0.00 0.00 0.00 0.00
de08 ~~ un05 0.01 0.00 0.00 0.00 0.00
pr07 ~~ ut05 0.01 0.00 0.00 0.00 0.00
un04 ~~ un09 0.01 0.00 0.00 0.00 0.00
co09 ~~ fa10 0.01 0.00 0.00 0.00 0.00
pr10 ~~ de09 0.00 0.00 0.00 0.00 0.00
de07 ~~ un10 0.00 0.00 0.00 0.00 0.00
un06 ~~ un11 0.00 0.00 0.00 0.00 0.00
ut04 ~~ fa09 0.00 0.00 0.00 0.00 0.00
ut04 ~~ de06 0.00 0.00 0.00 0.00 0.00
co02 ~~ co03 0.00 0.00 0.00 0.00 0.00
ut06 ~~ ut12 0.00 0.00 0.00 0.00 0.00
ut04 ~~ fa10 0.00 0.00 0.00 0.00 0.00
pr06 ~~ co09 0.00 0.00 0.00 0.00 0.00
de10 ~~ un06 0.00 0.00 0.00 0.00 0.00
pr02 ~~ ut06 0.00 0.00 0.00 0.00 0.00
un06 ~~ un09 0.00 0.00 0.00 0.00 0.00
fa06 ~~ un11 0.00 0.00 0.00 0.00 0.00
ut03 ~~ un08 0.00 0.00 0.00 0.00 0.00
pr07 ~~ ut03 0.00 0.00 0.00 0.00 0.00
pr10 ~~ un07 0.00 0.00 0.00 0.00 0.00
pr08 ~~ de08 0.00 0.00 0.00 0.00 0.00
pr01 ~~ fa09 0.00 0.00 0.00 0.00 0.00
fa08 ~~ de02 0.00 0.00 0.00 0.00 0.00
pr02 ~~ co03 0.00 0.00 0.00 0.00 0.00
fa01 ~~ de06 0.00 0.00 0.00 0.00 0.00
fa01 ~~ un07 0.00 0.00 0.00 0.00 0.00
fa06 ~~ de05 0.00 0.00 0.00 0.00 0.00
fa04 ~~ de08 0.00 0.00 0.00 0.00 0.00
co01 ~~ de02 0.00 0.00 0.00 0.00 0.00
fa05 ~~ un12 0.00 0.00 0.00 0.00 0.00
co01 ~~ fa08 0.00 0.00 0.00 0.00 0.00
ut08 ~~ un06 0.00 0.00 0.00 0.00 0.00
pr06 ~~ un11 0.00 0.00 0.00 0.00 0.00
fa08 ~~ de10 0.00 0.00 0.00 0.00 0.00
ut05 ~~ un08 0.00 0.00 0.00 0.00 0.00
pr07 ~~ pr10 0.00 0.00 0.00 0.00 0.00
co06 ~~ un07 0.00 0.00 0.00 0.00 0.00
co02 ~~ un06 0.00 0.00 0.00 0.00 0.00
co02 ~~ de07 0.00 0.00 0.00 0.00 0.00
co03 ~~ ut01 0.00 0.00 0.00 0.00 0.00
ut02 ~~ un05 0.00 0.00 0.00 0.00 0.00
co06 ~~ de03 0.00 0.00 0.00 0.00 0.00
co01 ~~ co10 0.00 0.00 0.00 0.00 0.00
co05 ~~ de08 0.00 0.00 0.00 0.00 0.00
ut08 ~~ de05 0.00 0.00 0.00 0.00 0.00
co02 ~~ fa09 0.00 0.00 0.00 0.00 0.00
co02 ~~ de09 0.00 0.00 0.00 0.00 0.00
co10 ~~ ut11 0.00 0.00 0.00 0.00 0.00
ut05 ~~ fa04 0.00 0.00 0.00 0.00 0.00
pr02 ~~ co08 0.00 0.00 0.00 0.00 0.00
de02 ~~ un03 0.00 0.00 0.00 0.00 0.00
co06 ~~ de02 0.00 0.00 0.00 0.00 0.00
ut06 ~~ fa09 0.00 0.00 0.00 0.00 0.00
co06 ~~ ut07 0.00 0.00 0.00 0.00 0.00
ut04 ~~ fa01 0.00 0.00 0.00 0.00 0.00
co06 ~~ fa05 0.00 0.00 0.00 0.00 0.00
co10 ~~ un11 0.00 0.00 0.00 0.00 0.00
pr10 ~~ ut11 0.00 0.00 0.00 0.00 0.00
de01 ~~ de07 0.00 0.00 0.00 0.00 0.00

Factors correlations:

modif3 %>% 
  filter(op == "~~" & 
           lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN") &
           rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) %>% 
  kable(digits = 2,
        col.names = c("Factor", "", "Factor", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))
Factor Factor Modification Index epc sepc.lv sepc.all sepc.nox
FA ~~ UN 18.86 -0.12 -0.24 -0.24 -0.24
PR ~~ UT 16.05 0.07 0.62 0.62 0.62
CO ~~ UT 13.65 -0.07 -0.24 -0.24 -0.24
FA ~~ DE 12.35 0.06 0.38 0.38 0.38
PR ~~ DE 9.73 -0.05 -1.22 -1.22 -1.22
PR ~~ FA 7.60 -0.06 -0.36 -0.36 -0.36
CO ~~ FA 2.64 0.04 0.10 0.10 0.10
DE ~~ UN 1.88 0.02 0.13 0.13 0.13
UT ~~ DE 1.58 -0.02 -0.16 -0.16 -0.16
PR ~~ CO 1.02 0.02 0.15 0.15 0.15
CO ~~ DE 0.67 0.01 0.10 0.10 0.10
PR ~~ UN 0.55 0.01 0.08 0.08 0.08
UT ~~ FA 0.23 -0.01 -0.03 -0.03 -0.03
UT ~~ UN 0.00 0.00 0.00 0.00 0.00
CO ~~ UN 0.00 0.00 0.00 0.00 0.00

Modificated model

mdl4 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
de05 ~~ de09
pr05 ~~ de06
fa02 ~~ fa08
pr05 ~~ ut11
fa02 ~~ fa09
fa08 ~~ fa09
ut11 ~~ de06
"
model4 <- cfa(mdl4, taia %>% select(all_of(taia_items_2)))
summary(model4)
## lavaan 0.6-8 ended normally after 48 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       127
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              4321.703
##   Degrees of freedom                              1526
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.728    0.056   13.027    0.000
##     pr05              0.717    0.063   11.369    0.000
##     pr06              0.811    0.062   13.157    0.000
##     pr07              1.054    0.062   17.060    0.000
##     pr08              0.859    0.050   17.015    0.000
##     pr09              0.731    0.055   13.317    0.000
##     pr10              0.668    0.061   11.018    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.892    0.061   14.528    0.000
##     co03              0.651    0.061   10.716    0.000
##     co04              0.475    0.065    7.307    0.000
##     co05              1.085    0.065   16.579    0.000
##     co06              0.859    0.066   13.092    0.000
##     co08              0.435    0.062    6.992    0.000
##     co09              0.973    0.063   15.443    0.000
##     co10              0.833    0.065   12.835    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.075    0.054   19.729    0.000
##     ut03              0.668    0.062   10.843    0.000
##     ut04              0.634    0.062   10.237    0.000
##     ut05              0.967    0.065   14.801    0.000
##     ut06              1.006    0.058   17.389    0.000
##     ut07              0.796    0.062   12.825    0.000
##     ut08              0.808    0.057   14.153    0.000
##     ut09              0.946    0.063   14.994    0.000
##     ut11              0.748    0.063   11.840    0.000
##     ut12              0.978    0.061   15.911    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.416    0.061    6.865    0.000
##     fa04              0.431    0.055    7.804    0.000
##     fa05              1.017    0.051   20.123    0.000
##     fa06              0.736    0.052   14.240    0.000
##     fa08              0.374    0.058    6.391    0.000
##     fa09              0.522    0.060    8.737    0.000
##     fa10              0.755    0.058   12.950    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.271    0.106   12.039    0.000
##     de03              1.149    0.105   10.967    0.000
##     de05              0.939    0.098    9.611    0.000
##     de06              1.001    0.096   10.446    0.000
##     de07              0.958    0.088   10.934    0.000
##     de08              1.146    0.096   11.925    0.000
##     de09              0.498    0.093    5.370    0.000
##     de10              1.319    0.109   12.146    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.941    0.000
##     un03              0.811    0.068   11.920    0.000
##     un04              1.022    0.062   16.533    0.000
##     un05              1.140    0.062   18.334    0.000
##     un06              0.779    0.072   10.820    0.000
##     un07              1.035    0.067   15.354    0.000
##     un08              1.118    0.066   16.979    0.000
##     un09              1.084    0.070   15.410    0.000
##     un10              1.046    0.066   15.909    0.000
##     un11              1.188    0.068   17.435    0.000
##     un12              1.061    0.064   16.565    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.769    0.062   12.436    0.000
##     UT                0.829    0.061   13.615    0.000
##     FA                0.836    0.066   12.631    0.000
##     DE                0.809    0.067   12.017    0.000
##     UN                0.417    0.054    7.689    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##  .de05 ~~                                             
##    .de09              0.701    0.062   11.257    0.000
##  .pr05 ~~                                             
##    .de06              0.601    0.054   11.139    0.000
##  .fa02 ~~                                             
##    .fa08              0.645    0.063   10.288    0.000
##  .pr05 ~~                                             
##    .ut11              0.581    0.056   10.365    0.000
##  .fa02 ~~                                             
##    .fa09              0.611    0.062    9.808    0.000
##  .fa08 ~~                                             
##    .fa09              0.562    0.060    9.417    0.000
##  .ut11 ~~                                             
##    .de06              0.518    0.054    9.624    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.373    0.029   12.957    0.000
##    .pr02              0.622    0.042   14.867    0.000
##    .pr05              1.022    0.067   15.227    0.000
##    .pr06              0.752    0.051   14.843    0.000
##    .pr07              0.570    0.042   13.729    0.000
##    .pr08              0.382    0.028   13.748    0.000
##    .pr09              0.590    0.040   14.813    0.000
##    .pr10              0.805    0.053   15.170    0.000
##    .co01              0.525    0.040   13.109    0.000
##    .co02              0.578    0.042   13.839    0.000
##    .co03              0.775    0.052   14.985    0.000
##    .co04              1.036    0.067   15.436    0.000
##    .co05              0.486    0.039   12.396    0.000
##    .co06              0.760    0.053   14.404    0.000
##    .co08              0.958    0.062   15.464    0.000
##    .co09              0.542    0.041   13.327    0.000
##    .co10              0.760    0.052   14.484    0.000
##    .ut01              0.440    0.033   13.477    0.000
##    .ut02              0.341    0.028   12.367    0.000
##    .ut03              0.929    0.061   15.258    0.000
##    .ut04              0.960    0.063   15.320    0.000
##    .ut05              0.840    0.057   14.633    0.000
##    .ut06              0.527    0.038   13.827    0.000
##    .ut07              0.858    0.057   15.003    0.000
##    .ut08              0.670    0.045   14.771    0.000
##    .ut09              0.773    0.053   14.588    0.000
##    .ut11              1.119    0.073   15.242    0.000
##    .ut12              0.683    0.048   14.347    0.000
##    .fa01              0.371    0.036   10.220    0.000
##    .fa02              1.245    0.080   15.465    0.000
##    .fa04              1.019    0.066   15.385    0.000
##    .fa05              0.343    0.036    9.613    0.000
##    .fa06              0.708    0.050   14.250    0.000
##    .fa08              1.171    0.076   15.502    0.000
##    .fa09              1.162    0.076   15.285    0.000
##    .fa10              0.958    0.066   14.588    0.000
##    .de01              0.794    0.054   14.778    0.000
##    .de02              0.676    0.049   13.906    0.000
##    .de03              0.889    0.061   14.605    0.000
##    .de05              0.992    0.066   15.059    0.000
##    .de06              0.964    0.064   14.947    0.000
##    .de07              0.626    0.043   14.620    0.000
##    .de08              0.581    0.041   14.007    0.000
##    .de09              1.334    0.086   15.587    0.000
##    .de10              0.690    0.050   13.803    0.000
##    .un01              0.497    0.035   14.366    0.000
##    .un02              0.399    0.030   13.228    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.423    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.149    0.075   15.375    0.000
##    .un07              0.731    0.050   14.739    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.726    0.000
##    .un10              0.657    0.045   14.603    0.000
##    .un11              0.584    0.041   14.092    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.059    0.017    3.421    0.001
##    .CO                0.314    0.038    8.159    0.000
##    .UT                0.291    0.033    8.734    0.000
##    .FA                0.460    0.049    9.428    0.000
##    .DE                0.051    0.014    3.767    0.000
##    .UN                0.514    0.055    9.332    0.000
##     DT                0.540    0.057    9.408    0.000
tibble(
  `Model 4` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model4,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()
Model 4 Value
Chi-Squared 4321.7034
DF 1526.0000
p 0.0000
GFI 0.7210
AGFI 0.6978
CFI 0.8085
TLI 0.7997
SRMR 0.0961
RMSEA 0.0608

Standardized solution:

smodel4 <- standardizedsolution(model4)

Loadings:

smodel4 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Item Loading SE z p CI lower bound CI upper bound
PR =~ pr01 0.785 0.020 38.709 0 0.745 0.825
PR =~ pr02 0.581 0.032 18.004 0 0.518 0.645
PR =~ pr05 0.481 0.035 13.946 0 0.414 0.549
PR =~ pr06 0.586 0.032 18.316 0 0.524 0.649
PR =~ pr07 0.734 0.024 31.182 0 0.688 0.780
PR =~ pr08 0.732 0.024 30.979 0 0.686 0.779
PR =~ pr09 0.593 0.032 18.705 0 0.531 0.655
PR =~ pr10 0.499 0.036 13.800 0 0.429 0.570
CO =~ co01 0.740 0.024 30.666 0 0.692 0.787
CO =~ co02 0.683 0.027 24.845 0 0.629 0.737
CO =~ co03 0.508 0.037 13.897 0 0.436 0.579
CO =~ co04 0.348 0.043 8.185 0 0.265 0.432
CO =~ co05 0.778 0.022 35.752 0 0.735 0.821
CO =~ co06 0.617 0.031 19.807 0 0.556 0.678
CO =~ co08 0.333 0.043 7.754 0 0.249 0.418
CO =~ co09 0.725 0.025 28.999 0 0.676 0.774
CO =~ co10 0.605 0.032 19.048 0 0.543 0.668
UT =~ ut01 0.775 0.020 37.826 0 0.735 0.815
UT =~ ut02 0.832 0.017 49.845 0 0.799 0.864
UT =~ ut03 0.491 0.036 13.553 0 0.420 0.562
UT =~ ut04 0.466 0.037 12.469 0 0.392 0.539
UT =~ ut05 0.651 0.028 23.122 0 0.596 0.706
UT =~ ut06 0.748 0.022 33.641 0 0.705 0.792
UT =~ ut07 0.573 0.032 17.696 0 0.510 0.637
UT =~ ut08 0.626 0.030 21.160 0 0.568 0.684
UT =~ ut09 0.659 0.028 23.749 0 0.604 0.713
UT =~ ut11 0.499 0.034 14.863 0 0.433 0.564
UT =~ ut12 0.694 0.026 27.020 0 0.643 0.744
FA =~ fa01 0.832 0.019 42.754 0 0.794 0.871
FA =~ fa02 0.323 0.044 7.419 0 0.237 0.408
FA =~ fa04 0.364 0.042 8.629 0 0.281 0.447
FA =~ fa05 0.847 0.019 45.113 0 0.810 0.883
FA =~ fa06 0.625 0.031 20.189 0 0.565 0.686
FA =~ fa08 0.301 0.044 6.834 0 0.215 0.388
FA =~ fa09 0.405 0.041 9.915 0 0.325 0.485
FA =~ fa10 0.577 0.033 17.223 0 0.511 0.642
DE =~ de01 0.581 0.033 17.839 0 0.517 0.645
DE =~ de02 0.701 0.026 27.084 0 0.650 0.752
DE =~ de03 0.613 0.031 19.820 0 0.552 0.673
DE =~ de05 0.515 0.036 14.381 0 0.444 0.585
DE =~ de06 0.544 0.033 16.735 0 0.480 0.608
DE =~ de07 0.610 0.031 19.650 0 0.549 0.671
DE =~ de08 0.691 0.026 26.096 0 0.639 0.743
DE =~ de09 0.265 0.045 5.945 0 0.177 0.352
DE =~ de10 0.711 0.025 28.077 0 0.661 0.760
UN =~ un01 0.742 0.022 33.778 0 0.699 0.785
UN =~ un02 0.831 0.016 52.516 0 0.800 0.862
UN =~ un03 0.541 0.033 16.194 0 0.476 0.607
UN =~ un04 0.735 0.022 32.767 0 0.691 0.779
UN =~ un05 0.807 0.018 46.042 0 0.773 0.842
UN =~ un06 0.493 0.036 13.845 0 0.423 0.563
UN =~ un07 0.686 0.025 26.957 0 0.637 0.736
UN =~ un08 0.753 0.021 35.455 0 0.711 0.795
UN =~ un09 0.689 0.025 27.199 0 0.639 0.738
UN =~ un10 0.709 0.024 29.489 0 0.662 0.756
UN =~ un11 0.771 0.020 38.552 0 0.732 0.810
UN =~ un12 0.736 0.022 32.952 0 0.692 0.780
DT =~ PR 0.949 0.015 64.540 0 0.921 0.978
DT =~ CO 0.710 0.029 24.513 0 0.653 0.767
DT =~ UT 0.749 0.025 29.589 0 0.699 0.798
DT =~ FA 0.672 0.031 21.357 0 0.610 0.733
DT =~ DE 0.934 0.016 59.214 0 0.903 0.965
DT =~ UN 0.393 0.042 9.300 0 0.310 0.476

Covariances:

smodel4 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Factor Factor Covariance SE z p CI lower bound CI upper bound
de05 ~~ de09 0.609 0.029 21.091 0 0.553 0.666
pr05 ~~ de06 0.606 0.030 20.237 0 0.547 0.664
fa02 ~~ fa08 0.534 0.032 16.465 0 0.471 0.598
pr05 ~~ ut11 0.543 0.033 16.451 0 0.478 0.608
fa02 ~~ fa09 0.508 0.034 15.001 0 0.442 0.575
fa08 ~~ fa09 0.482 0.035 13.759 0 0.413 0.551
ut11 ~~ de06 0.499 0.035 14.067 0 0.429 0.568

Residuals:

smodel4 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )
Item Residual SE z p CI lower bound CI upper bound
pr01 0.384 0.032 12.040 0 0.321 0.446
pr02 0.662 0.038 17.637 0 0.589 0.736
pr05 0.768 0.033 23.137 0 0.703 0.834
pr06 0.656 0.038 17.466 0 0.582 0.730
pr07 0.461 0.035 13.339 0 0.393 0.529
pr08 0.463 0.035 13.380 0 0.396 0.531
pr09 0.649 0.038 17.260 0 0.575 0.722
pr10 0.751 0.036 20.758 0 0.680 0.821
co01 0.453 0.036 12.702 0 0.383 0.523
co02 0.534 0.038 14.214 0 0.460 0.607
co03 0.742 0.037 20.020 0 0.670 0.815
co04 0.879 0.030 29.669 0 0.821 0.937
co05 0.395 0.034 11.658 0 0.328 0.461
co06 0.619 0.038 16.102 0 0.544 0.695
co08 0.889 0.029 31.020 0 0.833 0.945
co09 0.474 0.036 13.089 0 0.403 0.545
co10 0.634 0.038 16.468 0 0.558 0.709
ut01 0.399 0.032 12.580 0 0.337 0.462
ut02 0.309 0.028 11.121 0 0.254 0.363
ut03 0.759 0.036 21.293 0 0.689 0.828
ut04 0.783 0.035 22.515 0 0.715 0.851
ut05 0.576 0.037 15.700 0 0.504 0.648
ut06 0.440 0.033 13.230 0 0.375 0.505
ut07 0.672 0.037 18.087 0 0.599 0.744
ut08 0.608 0.037 16.418 0 0.536 0.681
ut09 0.566 0.037 15.496 0 0.495 0.638
ut11 0.751 0.033 22.463 0 0.686 0.817
ut12 0.519 0.036 14.579 0 0.449 0.589
fa01 0.307 0.032 9.478 0 0.244 0.371
fa02 0.896 0.028 31.898 0 0.841 0.951
fa04 0.867 0.031 28.217 0 0.807 0.928
fa05 0.283 0.032 8.919 0 0.221 0.346
fa06 0.609 0.039 15.727 0 0.533 0.685
fa08 0.909 0.027 34.200 0 0.857 0.961
fa09 0.836 0.033 25.281 0 0.771 0.901
fa10 0.668 0.039 17.290 0 0.592 0.743
de01 0.662 0.038 17.495 0 0.588 0.737
de02 0.508 0.036 13.997 0 0.437 0.579
de03 0.624 0.038 16.480 0 0.550 0.699
de05 0.735 0.037 19.964 0 0.663 0.807
de06 0.704 0.035 19.883 0 0.634 0.773
de07 0.628 0.038 16.559 0 0.553 0.702
de08 0.522 0.037 14.260 0 0.450 0.594
de09 0.930 0.024 39.497 0 0.884 0.976
de10 0.495 0.036 13.747 0 0.424 0.565
un01 0.450 0.033 13.798 0 0.386 0.514
un02 0.309 0.026 11.737 0 0.257 0.361
un03 0.707 0.036 19.564 0 0.636 0.778
un04 0.460 0.033 13.962 0 0.396 0.525
un05 0.348 0.028 12.310 0 0.293 0.404
un06 0.757 0.035 21.539 0 0.688 0.826
un07 0.529 0.035 15.126 0 0.460 0.597
un08 0.433 0.032 13.545 0 0.370 0.496
un09 0.526 0.035 15.068 0 0.457 0.594
un10 0.497 0.034 14.565 0 0.430 0.564
un11 0.405 0.031 13.128 0 0.345 0.466
un12 0.458 0.033 13.931 0 0.394 0.523
PR 0.098 0.028 3.526 0 0.044 0.153
CO 0.496 0.041 12.038 0 0.415 0.576
UT 0.439 0.038 11.589 0 0.365 0.514
FA 0.549 0.042 13.001 0 0.466 0.632
DE 0.127 0.029 4.312 0 0.069 0.185
UN 0.846 0.033 25.464 0 0.781 0.911
DT 1.000 0.000 NA NA 1.000 1.000
semPaths(model4,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Validation

TAIA total score

taia %>% 
  select(id, all_of(taia_items_2)) %>% 
  pivot_longer(all_of(taia_items_2),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>% 
  group_by(id, subscale) %>% 
  summarise(total_score = sum(score)) %>% 
  ungroup() %>% 
  pivot_wider(id_cols = id,
              names_from = subscale,
              values_from = total_score) %>% 
  relocate(after = c(id, PR, CO, UT, FA, DE, UN)) %>% 
  mutate(DT = PR + CO + UT + FA + DE + UN) %>% 
  full_join(taia) -> taia
## `summarise()` regrouping output by 'id' (override with `.groups` argument)
## Joining, by = "id"
clrs <-
  c("darkred",
    "chocolate3",
    "goldenrod3",
    "darkgreen",
    "darkblue",
    "purple4")

Correlations with General Trust Scale

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>%
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_l
taia_l %>% 
  ggplot(aes(score, gt_score, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale, scales = "free") +
  scale_color_manual(values = clrs) +
  guides(color = FALSE) +
  labs(x = "TAIA subscale total score",
       y = "General Trust Scale total score",
       title = "Corelations between General Trust and TAIA subscales") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'

Predictability

cor.test(taia$PR, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$gt_score
## t = 3.512, df = 493, p-value = 0.0004856
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0690492 0.2410450
## sample estimates:
##       cor 
## 0.1562312

Consistency

cor.test(taia$CO, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$gt_score
## t = 3.842, df = 493, p-value = 0.000138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08362567 0.25480562
## sample estimates:
##       cor 
## 0.1705018

Utility

cor.test(taia$UT, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$gt_score
## t = 2.4679, df = 493, p-value = 0.01393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02255584 0.19668679
## sample estimates:
##      cor 
## 0.110469

Faith

cor.test(taia$FA, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$gt_score
## t = 1.6968, df = 493, p-value = 0.09036
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01201538  0.16323459
## sample estimates:
##        cor 
## 0.07619805

Dependability

cor.test(taia$DE, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$gt_score
## t = 4.3405, df = 493, p-value = 1.726e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1055064 0.2753324
## sample estimates:
##       cor 
## 0.1918551

Understanding

cor.test(taia$UN, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$gt_score
## t = 3.5643, df = 493, p-value = 0.0004003
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.07136383 0.24323469
## sample estimates:
##       cor 
## 0.1584997

TAIA

taia %>% 
  ggplot(aes(DT, gt_score)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "TAIA score", y = "General Trust Score",
       title = "Correlation between General Trust and TAIA") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'

cor.test(taia$DT, taia$gt_score)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$gt_score
## t = 4.6048, df = 493, p-value = 5.261e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1170296 0.2860807
## sample estimates:
##       cor 
## 0.2030679

Correlations with questions

taia_l %>% 
  ggplot(aes(score, n_dighelp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Number of digital helpers",
       title = "Correlation TAIA subscales with number of digital helpers") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).

cor.test(taia$PR, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$n_dighelp
## t = 1.1715, df = 335, p-value = 0.2422
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04325762  0.16955094
## sample estimates:
##        cor 
## 0.06387275
cor.test(taia$CO, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$n_dighelp
## t = -0.96471, df = 335, p-value = 0.3354
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.15857803  0.05450713
## sample estimates:
##         cor 
## -0.05263456
cor.test(taia$UT, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$n_dighelp
## t = 1.3367, df = 335, p-value = 0.1822
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03426592  0.17828379
## sample estimates:
##       cor 
## 0.0728359
cor.test(taia$FA, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$n_dighelp
## t = -0.70154, df = 335, p-value = 0.4835
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1445447  0.0688158
## sample estimates:
##         cor 
## -0.03830097
cor.test(taia$DE, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$n_dighelp
## t = 1.1408, df = 335, p-value = 0.2548
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04492669  0.16792625
## sample estimates:
##        cor 
## 0.06220708
cor.test(taia$UN, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$n_dighelp
## t = 1.4962, df = 335, p-value = 0.1355
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02558116  0.18668686
## sample estimates:
##       cor 
## 0.0814767
cor.test(taia$DT, taia$n_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$n_dighelp
## t = 1.0065, df = 335, p-value = 0.3149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05223317  0.16080034
## sample estimates:
##        cor 
## 0.05490843
taia_l %>%
  ggplot(aes(score, e_dighelp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with digital helpers experience",
       title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).

cor.test(taia$PR, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$e_dighelp
## t = 6.4594, df = 335, p-value = 3.701e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2342924 0.4245384
## sample estimates:
##       cor 
## 0.3327975
cor.test(taia$CO, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$e_dighelp
## t = 4.0996, df = 335, p-value = 5.199e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1144032 0.3179773
## sample estimates:
##      cor 
## 0.218567
cor.test(taia$UT, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$e_dighelp
## t = 5.5088, df = 335, p-value = 7.21e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1871349 0.3832429
## sample estimates:
##      cor 
## 0.288208
cor.test(taia$FA, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$e_dighelp
## t = 3.9708, df = 335, p-value = 8.772e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1076158 0.3117865
## sample estimates:
##       cor 
## 0.2120134
cor.test(taia$DE, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$e_dighelp
## t = 5.5689, df = 335, p-value = 5.265e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1901671 0.3859219
## sample estimates:
##       cor 
## 0.2910883
cor.test(taia$UN, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$e_dighelp
## t = 1.7091, df = 335, p-value = 0.08836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01400204  0.19784231
## sample estimates:
##        cor 
## 0.09297223
cor.test(taia$DT, taia$e_dighelp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$e_dighelp
## t = 6.3515, df = 335, p-value = 6.95e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2290252 0.4199649
## sample estimates:
##       cor 
## 0.3278389
taia_l %>% 
  ggplot(aes(score, n_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Number of social networks and social media",
       title = "Correlation TAIA subscales with number of social networks and social media") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$n_socnet
## t = 1.7563, df = 433, p-value = 0.07974
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.009995365  0.176726793
## sample estimates:
##        cor 
## 0.08410396
cor.test(taia$CO, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$n_socnet
## t = -0.18106, df = 433, p-value = 0.8564
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10263724  0.08538923
## sample estimates:
##          cor 
## -0.008700912
cor.test(taia$UT, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$n_socnet
## t = 3.3825, df = 433, p-value = 0.0007836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06744242 0.25068403
## sample estimates:
##       cor 
## 0.1604453
cor.test(taia$FA, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$n_socnet
## t = 2.2881, df = 433, p-value = 0.02261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.01543692 0.20125076
## sample estimates:
##       cor 
## 0.1092986
cor.test(taia$DE, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$n_socnet
## t = 3.6857, df = 433, p-value = 0.000257
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08172823 0.26409798
## sample estimates:
##       cor 
## 0.1744083
cor.test(taia$UN, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$n_socnet
## t = 2.8836, df = 433, p-value = 0.004128
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04380848 0.22833688
## sample estimates:
##       cor 
## 0.1372634
cor.test(taia$DT, taia$n_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$n_socnet
## t = 3.3815, df = 433, p-value = 0.0007863
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06739663 0.25064092
## sample estimates:
##       cor 
## 0.1604005
taia_l %>% 
  ggplot(aes(score, f_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Frequency of social networks and social media use",
       title = "Correlation TAIA subscales with frequency of social networks and social media use") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$f_socnet
## t = 1.1135, df = 433, p-value = 0.2661
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04078842  0.14671995
## sample estimates:
##       cor 
## 0.0534368
cor.test(taia$CO, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$f_socnet
## t = 0.83383, df = 433, p-value = 0.4048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05418487  0.13355691
## sample estimates:
##       cor 
## 0.0400394
cor.test(taia$UT, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$f_socnet
## t = -0.18344, df = 433, p-value = 0.8545
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10275052  0.08527558
## sample estimates:
##          cor 
## -0.008815392
cor.test(taia$FA, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$f_socnet
## t = 0.096068, df = 433, p-value = 0.9235
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08944245  0.09859416
## sample estimates:
##         cor 
## 0.004616666
cor.test(taia$DE, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$f_socnet
## t = 0.53504, df = 433, p-value = 0.5929
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06848192  0.11943552
## sample estimates:
##        cor 
## 0.02570387
cor.test(taia$UN, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$f_socnet
## t = -0.29625, df = 433, p-value = 0.7672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10811106  0.07989175
## sample estimates:
##         cor 
## -0.01423547
cor.test(taia$DT, taia$f_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$f_socnet
## t = 0.36218, df = 433, p-value = 0.7174
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07674342  0.11124072
## sample estimates:
##        cor 
## 0.01740244
taia_l %>% 
  ggplot(aes(score, e_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with recommender systems experience",
       title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$e_socnet
## t = 4.6159, df = 433, p-value = 5.164e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1250906 0.3043865
## sample estimates:
##       cor 
## 0.2165639
cor.test(taia$CO, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$e_socnet
## t = 5.3414, df = 433, p-value = 1.493e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1583093 0.3348224
## sample estimates:
##       cor 
## 0.2486289
cor.test(taia$UT, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$e_socnet
## t = 4.0939, df = 433, p-value = 5.061e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1008503 0.2819433
## sample estimates:
##       cor 
## 0.1930402
cor.test(taia$FA, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$e_socnet
## t = 3.2486, df = 433, p-value = 0.00125
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06111661 0.24472172
## sample estimates:
##       cor 
## 0.1542505
cor.test(taia$DE, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$e_socnet
## t = 5.5098, df = 433, p-value = 6.177e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1659355 0.3417581
## sample estimates:
##       cor 
## 0.2559625
cor.test(taia$UN, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$e_socnet
## t = 1.7866, df = 433, p-value = 0.0747
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.008545219  0.178131408
## sample estimates:
##       cor 
## 0.0855438
cor.test(taia$DT, taia$e_socnet)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$e_socnet
## t = 5.4871, df = 433, p-value = 6.965e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1649116 0.3408280
## sample estimates:
##       cor 
## 0.2549784

Self driving cars and education AI

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, selfdrexp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of selfdriving car experience",
       title = "Correlation TAIA subscales with selfdriving car experience") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).

cor.test(taia$PR, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$selfdrexp
## t = 0.84968, df = 11, p-value = 0.4136
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3507759  0.7030276
## sample estimates:
##       cor 
## 0.2481747
cor.test(taia$CO, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$selfdrexp
## t = -0.35361, df = 11, p-value = 0.7303
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6207428  0.4725725
## sample estimates:
##        cor 
## -0.1060178
cor.test(taia$UT, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$selfdrexp
## t = 0.99003, df = 11, p-value = 0.3434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3145201  0.7230638
## sample estimates:
##       cor 
## 0.2860336
cor.test(taia$FA, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$selfdrexp
## t = -0.49537, df = 11, p-value = 0.6301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6461169  0.4389957
## sample estimates:
##        cor 
## -0.1477201
cor.test(taia$DE, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$selfdrexp
## t = -0.3201, df = 11, p-value = 0.7549
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6145242  0.4803441
## sample estimates:
##         cor 
## -0.09606636
cor.test(taia$UN, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$selfdrexp
## t = -0.11887, df = 11, p-value = 0.9075
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5754466  0.5255391
## sample estimates:
##         cor 
## -0.03581772
cor.test(taia$DT, taia$selfdrexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$selfdrexp
## t = 0.17179, df = 11, p-value = 0.8667
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5139042  0.5860113
## sample estimates:
##        cor 
## 0.05172814
taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, selfdrsafe, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of selfdriving car safety",
       title = "Correlation TAIA subscales with selfdriving car safety") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).

cor.test(taia$PR, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$selfdrsafe
## t = -0.13243, df = 11, p-value = 0.897
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5781736  0.5225747
## sample estimates:
##         cor 
## -0.03989866
cor.test(taia$CO, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$selfdrsafe
## t = 0.31876, df = 11, p-value = 0.7559
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4806533  0.6142740
## sample estimates:
##        cor 
## 0.09566808
cor.test(taia$UT, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$selfdrsafe
## t = -0.15671, df = 11, p-value = 0.8783
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5830211  0.5172388
## sample estimates:
##         cor 
## -0.04719733
cor.test(taia$FA, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$selfdrsafe
## t = 0.98457, df = 11, p-value = 0.346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3159418  0.7223097
## sample estimates:
##       cor 
## 0.2845836
cor.test(taia$DE, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$selfdrsafe
## t = -0.91642, df = 11, p-value = 0.3791
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7127275  0.3336096
## sample estimates:
##        cor 
## -0.2663311
cor.test(taia$UN, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$selfdrsafe
## t = -1.0936, df = 11, p-value = 0.2975
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7369795  0.2874227
## sample estimates:
##        cor 
## -0.3131557
cor.test(taia$DT, taia$selfdrsafe)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$selfdrsafe
## t = -0.34044, df = 11, p-value = 0.7399
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6183078  0.4756360
## sample estimates:
##        cor 
## -0.1021088
taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, eduaiexp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with education AI experience",
       title = "Correlation TAIA subscales with experience of dealing with education AI") +
  theme(plot.title = element_text(hjust = .5))
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2298 rows containing non-finite values (stat_smooth).
## Warning: Removed 2298 rows containing missing values (geom_point).

cor.test(taia$PR, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$eduaiexp
## t = 5.7393, df = 110, p-value = 8.512e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3232882 0.6111631
## sample estimates:
##      cor 
## 0.480047
cor.test(taia$CO, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$eduaiexp
## t = 4.8673, df = 110, p-value = 3.807e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2553469 0.5625701
## sample estimates:
##       cor 
## 0.4209573
cor.test(taia$UT, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$eduaiexp
## t = 4.1902, df = 110, p-value = 5.648e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1991635 0.5207166
## sample estimates:
##       cor 
## 0.3710084
cor.test(taia$FA, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$eduaiexp
## t = 3.4196, df = 110, p-value = 0.0008808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1320205 0.4685858
## sample estimates:
##       cor 
## 0.3099826
cor.test(taia$DE, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$eduaiexp
## t = 6.8158, df = 110, p-value = 5.253e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3997687 0.6633847
## sample estimates:
##       cor 
## 0.5449038
cor.test(taia$UN, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$eduaiexp
## t = 3.3073, df = 110, p-value = 0.001273
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1219943 0.4605948
## sample estimates:
##       cor 
## 0.3007422
cor.test(taia$DT, taia$eduaiexp)
## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$eduaiexp
## t = 6.6377, df = 110, p-value = 1.249e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3876975 0.6553096
## sample estimates:
##       cor 
## 0.5347814

Demographic data analysis

Number of participants

nrow(taia)
## [1] 495

Gender

table(taia$sex)
## 
##   f   m 
## 233 262
round(table(taia$sex) / length(taia$sex), 2)
## 
##    f    m 
## 0.47 0.53
prop.test(x = c(233, 0.54*495), n = c(495, 495))
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(233, 0.54 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.133451569 -0.005134289
## sample estimates:
##    prop 1    prop 2 
## 0.4707071 0.5400000
prop.test(x = c(262, 0.46*495), n = c(495, 495))
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(262, 0.46 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  0.005134289 0.133451569
## sample estimates:
##    prop 1    prop 2 
## 0.5292929 0.4600000

Age

taia %>% 
  ggplot(aes(age)) +
  geom_histogram(binwidth = 1, fill = "black") +
  facet_grid(sex ~ .,
             labeller = labeller(sex = c(f = "Females", m = "Males"))) +
  scale_x_continuous(breaks = seq(20, 80, 5)) + 
  labs(x = "Age", y = "Count",
       title = "Age distributions by gender")

taia %>% 
  group_by(sex) %>% 
  summarise(min = min(age),
            max = max(age),
            median = median(age),
            mean = mean(age))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 5
##   sex     min   max median  mean
##   <chr> <dbl> <dbl>  <dbl> <dbl>
## 1 f        17    81   34    35.5
## 2 m        18    61   35.5  36.1
t.test(taia$age ~ taia$sex)
## 
##  Welch Two Sample t-test
## 
## data:  taia$age by taia$sex
## t = -0.69515, df = 472.19, p-value = 0.4873
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.545920  1.215316
## sample estimates:
## mean in group f mean in group m 
##         35.4721         36.1374

Cities

taia %>% 
  mutate(city = tolower(city)) %>% 
  mutate(city = recode(city,
         "м" = "москва",
         "мск" = "москва",
         "saint-petersburg" = "санкт-петербург",
         "санкт петербург" = "санкт-петербург",
         "рф, санкт-петербург" = "санкт-петербург",
         "спб" = "санкт-петербург")) %>% 
  group_by(city) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable(col.names = c("City", "Num. of subjects"))
## `summarise()` ungrouping output (override with `.groups` argument)
City Num. of subjects
москва 81
санкт-петербург 28
екатеринбург 12
краснодар 12
казань 11
ростов-на-дону 11
самара 11
новосибирск 10
саратов 10
уфа 10
пермь 9
воронеж 8
пенза 7
челябинск 7
иркутск 5
калининград 5
красноярск 5
нижний новгород 5
саранск 5
ярославль 5
иваново 4
кемерово 4
кострома 4
омск 4
смоленск 4
томск 4
чебоксары 4
барнаул 3
брянск 3
владимир 3
волгоград 3
ижевск 3
йошкар-ола 3
киров 3
оренбург 3
петушки 3
таганрог 3
тамбов 3
тольятти 3
энгельс 3
архангельск 2
астрахань 2
белгород 2
бийск 2
братск 2
великий новгород 2
владивосток 2
ишим 2
красногорск 2
кропоткин 2
кузнецк 2
курск 2
магнитогорск 2
майкоп 2
мурманск 2
подольск 2
псков 2
рыбинск 2
рязань 2
сафоново 2
севастополь 2
стерлитамак 2
тула 2
тюмень 2
череповец 2
чита 2
шахты 2
- 1
1987 1
абакан 1
азов 1
алтай 1
анапа 1
анжеро-судженск 1
армавир 1
афанасьево 1
балашиха 1
белово 1
белореченск 1
березники 1
благовещенск 1
бронницы 1
бугульма 1
волгодонск 1
вологда 1
воркута 1
воткинск 1
всеволожск, ленинградская область 1
выборг 1
вязьма 1
г. миасс 1
геленджик 1
гусев 1
далматово 1
дербент 1
джанкой 1
дзержинск 1
дивногорск 1
добрянка 1
донецк 1
донецк рф 1
дубна 1
железногорск курская область 1
жигулевск 1
жуковский 1
зверево 1
зеленоград 1
златоуст 1
калтан 1
катайск 1
качуг 1
керчь 1
кизляр 1
кизнер 1
кинешма 1
киржач 1
кисловодск 1
кола 1
кондопога 1
королёв 1
красноусольский 1
красноуфимск 1
кривой рог 1
курган 1
луга 1
луховицы 1
мариинск 1
махачкала 1
минеральные воды 1
минск 1
мичуринск 1
набережные челны 1
нефтегорск 1
нижнекамск 1
новоалтайск. 1
новокузнецк 1
новокуйбышевск 1
новочебоксарск 1
няндома 1
орел 1
орехово-зуево 1
орловская область 1
пензенская область 1
первоуральск 1
пермский край, г.верещагино 1
покровское 1
полысаево 1
починок 1
прокопьевск 1
ревда 1
россия. ростовская область. город ростов-на-дону. 1
ростов 1
ростов на дону 1
ростов- на - дону 1
ряань 1
североморск 1
село тюбук 1
сибай 1
сіктівкар 1
славгород 1
сланцы 1
соликамск 1
сочи 1
ставрополь 1
старый оскол 1
суздаль 1
сургут 1
таганрог и макеевка 1
тайшет 1
татарстан 1
тверь 1
тобольск 1
ужур 1
ульяновск 1
уссурийск 1
усть-катав 1
хабаровск 1
чайковский 1
чапаевск 1
чкаловск 1
щёлково 1
электрогорск 1
электросталь 1
юрга 1

Education and Job

taia %>% 
  mutate(spec1 = tolower(spec1)) %>% 
  group_by(spec1) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()
## `summarise()` ungrouping output (override with `.groups` argument)
spec1 n
экономист 26
инженер 23
юриспруденция 14
юрист 14
бухгалтер 12
менеджмент 10
повар 9
психология 9
менеджер 6
экономика 6
нет 5
педагог 5
товаровед 5
менеджмент организации 4
программист 4
психолог 4
техник 4
технолог 4
швея 4
ветеринарный врач 3
геолог 3
инженер-конструктор 3
история 3
медсестра 3
механик 3
нет специальности 3
туризм 3
филолог 3
финансы и кредит 3
автомеханик 2
асу 2
биолог 2
бухгалтерия 2
водитель 2
геодезия 2
инженер механик 2
инженер-строитель 2
информатик-экономист 2
информатика и вычислительная техника 2
коррекционное образование 2
маркетолог 2
медик 2
оператор 2
оператор пк 2
перевод и переводоведение 2
программирование 2
радиотехник 2
сварщик 2
слесарь 2
строительство 2
технолог общественного питания 2
техносферная безопасность 2
товаровед-эксперт 2
торговля 2
учитель русского языка и литературы 2
филология 2
химия 2
эколог 2
экология 2
экономическое 2
электрик 2
электроэнергетика 2
- 1
(в школе) направление дизайна 1
9 классов, сейчас заканчиваю учебу в колледже 1
pr 1
radio 1
schienze del turismo 1
авто механик 1
автомеханник 1
автомобилестроение 1
авторихтовщик 1
агросервис транспорта и энергетики 1
актёр театра драмы и кино 1
артист 1
архитектор 1
архитектор-реставратор 1
бакалавр журналистики 1
безопасность жизнедеятельности 1
бизнес-информатика 1
биологические науки 1
биология 1
бух.учет 1
бухгалтер-экономист 1
бухгалтерский учет 1
бухгалтерский учет и аудит 1
бухучет 1
верстальщик 1
военный 1
вокалист 1
гму 1
горный инженер 1
гос служба 1
гостиничное дело 1
гостиничный сервис 1
государственное и муниципальное управление 1
гримёр 1
гум 1
дизайн 1
документоведение и архивоведение 1
документоведение и информационная аналитика 1
журналист 1
журналистика 1
зооинженер 1
иллюстратор 1
инженер автомобилестроения 1
инженер асои 1
инженер водоснабжения 1
инженер горный 1
инженер информационных технологий и систем 1
инженер конструктор 1
инженер конструктор технолог эва 1
инженер микроэлектроники 1
инженер по земельному кадастру 1
инженер связи 1
инженер строитель 1
инженер технолог 1
инженер-механик 1
инженер-экономист лесной промышленности и лесного хозяйства 1
инженер-электрик 1
информатик- экономист 1
информатика и икт 1
информационная безопасность 1
информационные системы 1
информационные системы в экономике 1
информационные системы и технологии 1
информационные технологии и системы 1
историк-исследователь 1
каменщик 1
кибернетик 1
кибернетика 1
коммерсант 1
коммерция 1
компьютерная инженерия 1
компьютерные системы управления 1
кондитер 1
консервный мастер 1
конструктор-дизайнер 1
культурология 1
лаборант 1
лечебное дело 1
лингвист-переводчик 1
логопедия 1
маляр 1
маркетинг (только начал обучение 1
мастер отделочных работ 1
мастер сельхоз производства 1
математика 1
математический факультет нгпу (неоконченное) 1
материаловедение 1
материаловедение и технология материалов 1
машинист 1
машинист крана 1
машины и технологии литейного производства 1
медицинская сестра 1
менеджер по персоналу 1
менеджер по продажам 1
менеджмент спорта и туризма 1
металловедение и термическая обработка металлов 1
металлургия 1
монтаж, наладка и эксплуатация электрического оборудования гражданских и промышленных зданий 1
монтажник 1
монтёр аэс 1
не училась 1
никакой 1
обработка металлов давлением 1
общеобразовательная школп 1
омд 1
омс 1
оператор эвм 1
отсутствует 1
пед 1
педагог-электротехник 1
педагогическое 1
педиатрия 1
переводчик 1
повар кондитер 1
повар-технолог 1
повор 1
помощник машиниста 1
право 1
правовед 1
преподаватель 1
преподаватель английского языка 1
преподаватель начальных классов 1
преподаватель рус. яз., лит-ры и ин. языка 1
преподаватель экономики 1
прессовщик изделий из пластмасс с умением работать на литьевых машинах 1
прикладная геодезия 1
прикладная информатика в экономике 1
программист в компьютерных сетях 1
программное обеспечение вычислительных систем 1
продавец 1
проектирование радио 1
проектирование, эксплуатация, инжиниринг аэс 1
промешленное и гражданское строительство 1
промышленное и гражданское строительство 1
промышленный дизайн 1
психология бак + персонология маг 1
рабочий 1
радиомонтажник 1
радиотехника 1
редактор (газетно-журнальное издательское дело) 1
режиссёр 1
режиссура кино и телевидения 1
ремонт железнодорожного пути и путёвого хозяйство 1
русский язык и литература 1
рыбмастер 1
связь 1
секретарь 1
сервис и техническая эксплуатация транспортных и технологических машин. 1
сети связи 1
сис админ 1
системный администратор 1
системотехник 1
систему автоматического управления, но не защитил диплом 1
слесарь мср 1
слесарь по ремонту подвижного состава 1
соц.работа 1
социальная педагогика 1
социальная работа 1
социально-гуманитарное направление 1
социология 1
специалист по сервису 1
специалист по таможенному оформлению 1
специальности нет 1
среднее профессиональное образование 1
станочник широкого профиля 1
старший техник по и ас 1
стилист парикмахер 1
страховое дело 1
строитель 1
строитель кровельщик отделочник 1
строительство железных дорог 1
строительство и эксплуатация зданий и сооружений 1
тактико-специальная подготовка 1
таможенное дело 1
таможенное доле 1
телекоммуникации 1
техник - строитель 1
техник по информационным системам 1
техник программист 1
техник эвм 1
техник-коммерсант 1
техник-связи 1
техник-строитель 1
технолог молока и молочной продукции 1
технолог органических веществ 1
технолог поп 1
технология интернет 1
технология продуктов общественного питания 1
технология художественной обработки материалов 1
токарь 1
у меня нет специальности 1
управление персоналом 1
учет и аудит 1
училась на педагога по истории, но не окончила (незаконченное высшее образование - более трех лет) 1
учитель английского языка 1
учитель истории 1
учитель математики 1
учитель матнматики и физики 1
учитель начальных классов 1
учитель права 1
учитель технологии 1
учитель физической культуры и обж 1
учитель французского и немецкого языка 1
фармация 1
фельдшер 1
финансы и экономика 1
фрезеровщик 1
химик 1
художник 1
художник-мультипсикатор 1
школа 1
школьник 1
штукатур-маляр 1
экономика и бухгалтерский учёт 1
экономист-математик 1
экономическая безопасность 1
экспертиза и управление недвижимостью 1
электогазосварщик 1
электро монтёр связи 1
электрогазосварщик 1
электромеханик 1
электромонтёр 1
электроника и наноэлектроника 1
электронщик 1
электросварщик 1
электроснабжение 1
энергетик 1
юридическое 1
юрисконсульт 1
юриспруденөия 1
taia %>% 
  mutate(spec2 = tolower(spec2)) %>% 
  mutate(spec2 = recode(spec2, "-2" = "NA")) %>% 
  group_by(spec2) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()
## `summarise()` ungrouping output (override with `.groups` argument)
spec2 n
NA 428
бухгалтер 4
экономист 3
юрист 3
метролог 2
сварщик 2
design 1
автоматизация м связь 1
администратор салона красоты 1
архитектор 1
асу 1
библиотековед-библиограф 1
бухучет 1
водитель троллейбуса 1
гос управление 1
графический дизайн 1
дефектология 1
инженер виноделия 1
иностранный язык 1
культуролог 1
логопед 1
мастер отделочных и строительных работ 1
машинист крана 1
медбрат 1
медицинская сестра 1
менеджмент 1
менеджмент в сфере туризма 1
механик 1
наладчик холодильных установок 1
налогообложение 1
общий и стратегический менеджмент 1
оператор котельной 1
парикмахер-универсал 1
педагогика 1
переводчик 1
повар 1
правовед 1
преподаватель 1
программист 1
психология (неоконченное образование) 1
радиомонтажник 1
редактор 1
реставратор 1
сестринское дело 1
специалист 1
специалист аэропорта 1
специалист по охране труда 1
специалист по туристическим услугам 1
строительство и эксплуатация зданий и сооружений 1
телефонист 1
техник 1
тренер-преподаватель 1
управление персоналом 1
ученый-агроном 1
филология 1
финансовый менеджер 1
финансы 1
экономика 1
юриспруденция 1

Comparison trust level of digital specialisations with other

specs <- c(
  "программист",
  "информатик-экономист",
  "информатика и вычислительная техника",
  "оператор пк",
  "программирование",
  "бизнес-информатика",
  "информатик- экономист",
  "информатика и икт",
  "информационная безопасность",
  "информационные системы",
  "информационные системы в экономике",
  "информационные системы и технологии",
  "информационные технологии и системы",
  "кибернетика",
  "компьютерная инженерия",
  "компьютерные системы управления",
  "прикладная информатика в экономике",
  "программист в компьютерных сетях",
  "программное обеспечение вычислительных систем",
  "сис админ",
  "системный администратор",
  "техник по информационным системам",
  "техник программист"
)
taia %>% 
  mutate(spec1 = tolower(spec1),
         spec2 = tolower(spec2),
         dig_spec = ifelse(spec1 %in% specs | spec2 %in% specs, TRUE, FALSE)) -> taia
t.test(taia$DT ~ taia$dig_spec)
## 
##  Welch Two Sample t-test
## 
## data:  taia$DT by taia$dig_spec
## t = -2.6551, df = 34.335, p-value = 0.01194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -25.820608  -3.435371
## sample estimates:
## mean in group FALSE  mean in group TRUE 
##            152.9849            167.6129

taia %>% 
  mutate(jobfield = tolower(jobfield)) %>% 
  group_by(jobfield) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()
## `summarise()` ungrouping output (override with `.groups` argument)
jobfield n
- 156
it 18
строительство 16
продажи 14
образование 12
торговля 12
не работаю 9
производство 9
транспорт 8
логистика 7
медицина 6
фриланс 6
охрана 5
самозанятый 5
финансы 5
жкх 4
информационные технологии 4
машиностроение 4
энергетика 4
_ 3
безопасность 3
графический дизайн 3
грузоперевозки 3
культура 3
металлургия 3
ржд 3
самозанятая 3
2
-1 2
hr 2
банк 2
бухгалтерия 2
здравоохранение 2
ит 2
обслуживание 2
общепит 2
пенсионер 2
продажа 2
проектирование 2
реклама 2
социальная 2
спорт 2
телекоммуникации 2
туризм 2
фрилансер 2
юридическая сфера 2
____ 1
1
—– 1
—————- 1
1
3-печать 1
it (1c) 1
it-сфера 1
it-технологии 1
it, коммуникации 1
orm/smm 1
smm 1
автомобильный бизнес 1
адвокат 1
атб 1
банковское дело 1
безопасность персонала 1
бизнес, торговля 1
биология, медицина 1
бухгалтер 1
бухгалтерский учет 1
в интернете 1
визажист 1
геодезия 1
госструктура 1
гостинечный бизнес 1
гостиничный бизнес 1
государственная служба 1
дизайн 1
дизайн интерьеров 1
добыча полезных ископаемых 1
иллюстрация 1
интернет 1
информатика 1
информационная безопасность 1
информационные технологии в медицине 1
ип 1
ип в сфере туризма 1
искусство 1
искусство и культура 1
кинопроизводство 1
колл центр 1
коммунальные услуги 1
консалтинг 1
литература, театр. 1
логистика складская 1
маркетинг 1
мебель 1
медиа 1
музыка 1
музыкальное образование 1
музыкант 1
мфц 1
мчс россии 1
налоговая сфера 1
научно-исследовательская деятельность, ветеринария, эпидемиология 1
недвижимость 1
нефте добыча 1
нефтяная 1
нефтянка 1
обслуживание и ремонт контрольно-кассовой техники 1
обслуживание населения 1
оказание услуг 1
онлайн-обучение 1
оптовая торговля 1
остин 1
охрана труда 1
охранная деятельность 1
пенсионерка 1
пивной блогер 1
пномышленность 1
право 1
программист 1
продажа кухонной мебели, дизайн 1
продажа мебели 1
производство мебели 1
производство металлоконструкций 1
производство продуктов питания 1
производство систем охлаждения 1
производство х/булочных изделий 1
промышленное производство 1
рабочий на заводе 1
развлечения 1
разметка информации 1
ремонт бытовой техники 1
ремонт электроники 1
ресторанный бизнес 1
рыболовство 1
самозанятый it специалист 1
связь 1
связь и телекоммуникации 1
сельское хозяйство и производство 1
склад 1
служба доставки 1
соц защита 1
социалка 1
стоматология 1
страхование 1
строителство 1
строительство и дизайн 1
строительство, ремонт 1
сфера жкх 1
сфера обслуживания 1
сфера розничных продаж 1
сфера услуг 1
такси 1
телекоммуникация 1
телекоммуникация. 1
телемуникация 1
тестирование 1
техника 1
технологии 1
типография 1
транпортные перевозки 1
умные охранные системы 1
университет 1
управление персоналом 1
услуги населению, бюджетная организация. 1
фармация 1
флористика 1
частный предприниматель 1
экологическое проектирование 1
экономика 1
эконофика 1
экстренная служба 1
электронная подпись 1
юзабилити 1
юриспруденция 1
я работаю в сфере it 1

Comparison trust level of digital job fields with other

jobs <- c(
  "it",
  "информационные технологии",
  "ит",
  "it (1c)",
  "it-сфера",
  "it-технологии",
  "it, коммуникации",
  "информатика",
  "информационная безопасность",
  "информационные технологии в медицине",
  "программист",
  "самозанятый it специалист",
  "я работаю в сфере it",
  "юзабилити",
  "техника",
  "технологии"
)
taia %>% 
  mutate(jobfield = tolower(jobfield),
         dig_job = ifelse(jobfield %in% jobs, TRUE, FALSE)) -> taia
t.test(taia$DT ~ taia$dig_job)
## 
##  Welch Two Sample t-test
## 
## data:  taia$DT by taia$dig_job
## t = -0.62167, df = 40.728, p-value = 0.5376
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.225044   8.058987
## sample estimates:
## mean in group FALSE  mean in group TRUE 
##            153.6332            157.2162